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 ϭ Ϯ ACKNOWLEDGEMENTS First and foremost I wish to express my sincere gratitude to my friends Peter Amon MAERERE and Hamis Daniel WAMBURA from the Department of crop science and Horticulture, SUA, who despite of being exhausted with their dissertation yet they could spare some time to pass through the manuscript and gave some constructive comments and criticisms. That enabled me to write the book in a much more professional way. I wish to gratefully acknowledge the support extended to me by all extension officers, Researchers family members and the department of soil science and geology, SUA. Their support enabled me to perform the work properly and to draft the manuscript in a much more precise manner. ϯ DEDICATION I dedicate this book to all farmers worldwide who are working tirelessly in the field to ensure ZRUOG¶VIRRGVHFXULW\, I am sure they have the utmost patience in a prudent path of being...Warriors in the garden rather than the gardeners in a war. ϰ  PREFACE The growing and steady increase of world population has led to a greater demand in quantity, quality and nutritional value of food much more than the world has ever produced before. Meeting this demand will require a 70% increase in food production and up to 50% increase investment for developing countries alone, this has challenged the conventional farming and agronomic methods some which have significantly failed to cope with the robust world population and consequently world market for agricultural commodities. These challenges have made it necessary to use new technologies like remote sensing (RS) in the agricultural sector. The use of RS is important because precision agriculture is data intensive. The aim of the author is to promote the use of the remote sensing technology (RS) and geographic information system (GIS) in agriculture and its benefits to farmers, especially the African farmers. Precision agriculture offers a diverse of advantages to its users, from farmers in the production fields to the end-consumers. People in the agriculture industry need to think of innovative ideas which will optimize opportunities in Agri sector and accommodate the new European Geostationary Navigation Overlay Service (EGNOS) and European Galileo Satellite System. You GRQ¶WKDYH to be a satellite specialist to incorporate this technology into your farm, all you need is the right information and the right people to do the work. This book will try to offer some of the information so as to make awareness to farmers, technicians and academicians. Precision agriculture is unavoidable because it ensures food security, geo-traceability, and sustainable agronomic methods. Agricultural management issues have a critical geographic dimension. RS technology enhances the storage and management of geographic information in order to analyze patterns, relationships, and trends for sounding agronomic decisions. Observing the colors of leaves or the overall appearances of the crop can determine the condition of plants. Remotely sensed images taken from  ϱ drones, aircrafts and satellites provide a means to assess field conditions without physically being there. This enables more precision and accuracy thus opening the way to a vibrant agriculture sector. This book will discuss a highly effective farming strategy which allow farmers to tend maize rust disease in the field by using RS technology and specifically the drone. This will help small holder farmers to increase productivity while lowering costs and minimising environmental impact. It is the hope of the author that, this book will act as a right hand methodology for all people who intend to incorporate RS and GIS technology in the farming industry and especially to the ³VOHHSLQJJLDQW¶¶$IULFDQDJULFXOWXUHLQGXVWU\ Agape PALILO 21st September 2017  ϲ FOREWORD The increase of world population, along with the poor agricultural practices leading to environmental destruction have made a need to upcoming technologies like remote sensing (RS) and geographic information system (GIS) in the agricultural sector. The use of RS is important because precision agriculture is data intensive. Agricultural management issues have a critical geographic dimension. RS technology enhances the storage and management of geographic information in order to analyze patterns, relationships, and trends for better decisions.Observing the colors of leaves or the overall appearances of plants FDQ GHWHUPLQH WKH SODQW¶V FRQGLWLRQ 5HPRWHO\ VHQVHG LPDJHV WDNHQ IURP GURQHV DLUFUDIWV DQG satellite provide a means to assess field conditions without physically touching them. This enables more precision and accuracy thus opening the way to precision agriculture. This book has been written to discuss a highly effective farming strategy which allows a farmer to tend maize rust disease in the fields by using RS technology and specifically a drone. This will help small holder farmers to increase productivity while lowering costs and minimising environmental impact. Peter Amon Maerere (Bsc, MCS).  ϳ TABLE OF CONTENTS ACKNOWLEDEMENTS ««««««««««««««««««««««««««....2 DEDICATION «««««««««««««««««««««««««««««««3 PREFACE ««««««««««««««««««««««««««««««««4 FOREWORD ««««««««««««««««««««««««««««««««6 TABLE OF CONTENTS «««««««««««««««««««««««««««7 PRECISION AGRICULTURE ««««««««««««««««««««««««....8 OPTIMIZATION OF GIS AND RS ««««««««««««««««««««««....11 MAIZE RUST DISEASE MANAGEMENT «««««««««««««««««««..24 DRONE OPERATIONS AND MECHANISMS IN THE FARM ««««««««««.31 CHALLENGES OF ADOPTING RS TECHNOLOGY IN THE AFRICAN AGRICULTURE INDUSTRY «««««««««««««««««««««««««««««««« 37 THE FUTURE OF DRONE TECHNOLOGY AND PRIVACY ISSUES ««««««««. 43 5()(5(1&(6««««««««««««««««««««««««««««««« ϴ CHAPTER 1 1.0 PRECISION AGRICULTURE 1.1 INTRODUCTION TO PRECISION AGRICULTURE (P.A) Precision farming (PA) or satellite farming or site specific crop management (SSCM) is an agronomic management method which focuses on monitoring, measuring and responding to inter and intra-field variabilities of the crop. Crop variability has both a spatial and temporal component which leads to statistical and computational treatments necessary. The main purpose of precision agriculture researches will be the ability to define a Decision Support System (DSS) on the farm management for sounding agronomic decisions with the goal of optimizing returns on inputs used while preserving environment sustainably. There has been quite some good effort on bringing about major changes to precision farming, however, there is a lot of work remaining to create an actual farmer friendly precision agriculture technology that could universally help all the farmers. 1.2 THE ESSENCE OF PRECISION AGRICULTURE TECHNOLOGY The potential benefits of precision agriculture containing precise and accurate soil-crop data which allows a farmer to improve the planning of their activities and inputs to achieve sounding agronomic methods. This may overall leads to improvement in the farm performance i.e. lower inputs of energy, fertiliser, seed etc. and basically financial performance i.e. specific yield, gross margin etc. In general Precision agriculture aims to optimize agronomic methods and practices with regard to ϵ  9 Crop science, Optimizing agronomic methods more closely to the inputs i.e. seeds, fertilizers and pesticides. 9 Soil science, protecting the environment by minimising the risks in the farming methods i.e. limiting leaching of nitrogen); 9 Farm economics: increasing yield per unit farmland making the farms much more efficient thus improved management of inputs and so increased returns. 1.3 HISTORY OF PRECISION AGRICULTURE TECHNOLOGY The concept of precision agriculture first emerged in the United States in the early 1980s. In 1985, researchers at the University of Minnesota were able to vary lime inputs in crop fields. It was also at this time that the practice of grid sampling appeared (applying a fixed grid of one sample per hectare). In the end of the 1980s, the technique was used to derive the first input recommendation maps for fertilizers and pH corrections. The use of yield sensors with GPS receivers, has been incorporated into agronomic methods ever since. In nowadays, such technology cover several million hectares of farms worldwide. In the American Midwest, it is associated with farmers who are maximizing profits and returns per unit area of a land by spending money only in areas that require inputs. This practice allows the farmer to vary the rate of inputs across the field according to the need identified by GPS guided Grid or Zone Sampling. Inputs that would have been spread in areas that don't need it can be placed in areas that do, thereby optimizing its use. The pioneer nations of this technology were the United States, Canada and Australia. In Europe, the United Kingdom was the first to adopt PA technology, followed by France around 1997-1998. In Latin America the leading country is Argentina, where it was introduced in the middle 1990s with the   ϭϬ support of the National Agricultural Technology Institute and in Africa the technology is widely used in the North African nations and in South Africa. 1.4 ECONOMIC IMPORTANCE OF PRECISION AGRICULTURE TECHNOLOGY Precision agriculture practices can significantly reduce the amount of inputs used, reducing costs while boosting yields and so increasing returns. Farmers can obtain reasonable return on their investment per unit area of a land. The second concern is environmental impacts-Applying the right amount of inputs in the right place and at the right time reduce pollution of environment and underground water, thus benefits crops, soils and groundwater. Consequently, precision agriculture has become a cornerstone of sustainable agriculture, since it respects crops, soils, farmers and the environment in general.  ϭϭ  CHAPTER TWO 2.0 OPTIMIZATION OF GEOGRAPHIC INFORMATION SYSTEM AND REMOTE SENSING TECHNOLOGY 2.1OPTIMIZATION GEOGRAPHIC INFORMATION SYSTEM (GIS) TECHNOLOGY Geographic Information System (GIS) includes a set of processes, executed on raw data to produce information which is used in decision making. It includes chain of steps which starts from observation, collection of data through analysis in order to achieve a purpose. An information system must have a full range of functions to achieve its purpose, including observation, measurement, description, explanation, forecasting and decision making. Geographic information system uses geographically reference data as well as non-spatial data and includes operations which support spatial analysis to achieve a common purpose. In GIS the common purpose is decision making for managing, use of land resources, transportation, retailing, oceans or any spatially distributed entities. In GIS the connection between the elements of the system is geography, e.g. location, proximity and spatial distribution. The system works with an aid of satellite navigation system which is composed of six orbital planes each surrounded by four satellites mounted on orbits in the outer space. Satellite navigation is a system of satellites that provide autonomous geo-spatial positioning with global coverage (usually termed as Global Navigation Satellite System, GNSS). It allows small electronic receivers to determine their position (in terms of longitude, latitude, and altitude) to within a few metres using time signals transmitted along a line-of-sight by radio from satellites. Receivers calculate the precise time as well as position, which can be used as a reference for scientific experiments.   ϭϮ Image 2.1 a set of processes of the Geographic Information System (GIS). 2.1.1 GLOBAL NAVIGATION SATELLITE SYSTEM (GNSS) There are currently two GNSS systems which are fully globally operational which are the United States Global Positioning System (GPS) and the Russian GLONASS. The European Galileo positioning system is currently being implemented. China is also in the process of expanding its regional navigation system (The Beidui) into the global Compass navigation system by 2020. The main advantages of satellite technologies in agriculture include high accuracy and repeatability of the same action year on year. These two fundamental advantages lead to valuable benefits in agriculture such as reduced waste through over-DSSOLFDWLRQ RI IHUWLOL]HU¶V DQG herbicides, reduced environmental impact, seed consumption, fuel and time savings, lower fatigue, extended equipment life and optimization of crop yields.   ϭϯ 2.1.2 EUROPEAN GEOSTATIONARY NAVIGATION OVERLAY SERVICE (EGNOS) (XURSHDQ*HRVWDWLRQDU\1DYLJDWLRQ2YHUOD\6HUYLFHZKLFK LV HVVHQWLDOO\(XURSH V µSUH-Galileo' system of satellite navigation deliver services based on GPS and GLONASS signals and augments them to increase the accuracy. This enables metre to metre-precision and path to path accuracy of up to 13-15cm opening the way to precision agriculture. This will allow a farmer to tend remotely sensed problems within the field with an acute eye vision. This highly effective farming strategy allows farmers to better allocate inputs, such as seeds and fertilisers, to increase productivity while lowering costs and minimising environmental impact. 2.1.3 GALILEO SYSTEM OF SATELLITE NAVIGATION Galileo is a satellite system currently being built by the EU aiming to be the single European GNSS. Up to now, GNSS users in Europe have had no alternative other than to use American GPS and Russian GLONASS satellite signals. The military operators of these systems can give no guarantee to maintain uninterrupted service. From there it emerged the need for a complete civilian and farmer friendly GNSS, so the European countries decided to launch Galileo. Meanwhile, satellite positioning has already become the standard and essential tool for navigating and related applications. As the use of satellite navigation spreads, the implications of signal failure increase, jeopardising not only the efficient running of transport systems, but also human safety. By being interoperable with GPS, Galileo aspires to be a new cornerstone of GNSS. This worldwide system will henceforth be under civilian control. And with its full complement of   ϭϰ satellites, more than the current GNSS systems, Galileo will allow positions to be determined accurately even in high-rise cities, where buildings obscure signals from today's satellites. Galileo will also offer several signal enhancements making the signal more easy to track and acquire and more resistant against interference and reflections. By placing satellites in orbits at a greater inclination to the equatorial plane, Galileo will also achieve better coverage at high latitudes, making it particularly suitable for operation over northern Europe, an area not well covered by current GPS signals. 2.1.4 EUROPEAN GEOSTATIONARY NAVIGATION OVERLAY SERVICE (EGNOS) CONCEPTS IN PRECISION AGRICULTURE Precision agriculture involves the use of satellite navigation sensors, aerial images, and other tools to determine optimum sowing density, fertiliser cover and other inputs. It also refers to the use of GNSS for supporting machine guidance, virtual fencing, and land parcel identification. These techniques allow farmers to save money, reduce their impact on the environment and increase their productivity. EGNOS can offer an affordable precision solution. EGNOS can support some activities in agriculture which some of them includes Variable ploughing, seeding and spraying, Variable Rate Technology (VRT), Tractor guidance, Individual livestock positioning, Virtual fencing, Land parcel identification and geo-traceability, Post-harvest pick-up, Supervised livestock tracking, Field measurement, Field boundary mapping and updating.   ϭϱ 2.1.5 THE ADVANTAGES OF EGNOS IN AGRICULTURE The European Geostationary Navigation Overlay Service will allow farmers to save money, reduce environment impact and destruction while increase the productivity so simplifying agriculture and making it attractive to the youths. EGNOS has the other advantages including to enhance precision, eliminate waste and over-application of inputs i.e. fertilisers and herbicides, saves time, Reduce fatigue, Extend equipment lifetime by optimising its use, Provide geo-traceability, Optimise crop yields, Increase profit margins. The above advantages will therefore help the farmer to tend remotely sensed images from the field thus simplifying to solve farm problems such as drought, pests and diseases. Almost all the diseases which affect the plant tissues can be sensed through air borne sensors but this book will discuss in details the common maize rust disease (Puccinia sorghi) which is a great threat to small holder farmers in some parts of Africa. 2.2 OPTIMIZATION OF REMOTE SENSING TECHNOLOGY Remote sensing is a process of observing things without physically touching them. The process is aided through sensors designated depending on a specific task intended to be carried. Remote sensors can be either passive or active. Passive sensors respond to external stimuli such that they record UDGLDWLRQWKDWLVUHIOHFWHGIURP(DUWK¶VVXUIDFHXVXDOO\IURPWKHVXQ%HFDXVHRI this, passive sensors can only be used to collect data during daylight hours.   ϭϲ In contrast, active sensors use internal stimuli to collect data about Earth. For example, a laserbeam remote sensing system projects a laser onto the surface of Earth and measures the time that it takes for the laser to reflect back to its sensor. Among the two types of remote sensing systems the most common used in agriculture is a passive system which senses the electromagnetic energy reflected from plants through electromagnetic sensors. The sensors can be installed on satellites, manned or unmanned aircraft, or right on farm equipment. Figure 2.2 Energy source and interaction  ϭϳ  The choice of a remote sensing system for a particular application is guided by several factors. These includes spatial resolution, spectral resolution, radiometric resolution, and temporal resolution. Spatial resolution denotes the minimum size of an object that can be noticed in an image. A pixel the basic unit in an image. One-meter spatial resolution implies each pixel image stand for an area of one square meter. As the area represented by one pixel decreases, the resolution of the image increases. Spectral resolution refers to the number and the wavelength width of each band (a narrow portion of the electromagnetic spectrum). Higher spectral resolution images are more preferable for distinction of shorter wavelength widths. The images from Multi-spectral camera can measure several wavelength bands such as visible green or NIR. Hyperspectral imagery measures energy in narrower and more numerous bands than multi-spectral imagery. The narrow bands of hyperspectral imagery are more sensitive to variations in energy wavelengths and therefore have a greater potential to detect crop stress than multi-spectral imagery. Multi-spectral and hyperspectral imagery are used together to provide a more complete picture of crop conditions. Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels. The higher the radiometric resolution of a remote sensor, the more sensitive it is to detecting small differences in reflectance values. Higher radiometric resolution allows a remote sensor to provide a more precise picture of a specific portion of the electromagnetic spectrum.   ϭϴ Temporal resolution refers to how often a remote sensing platform can provide coverage of an area. Geo-stationary satellites can provide continuous sensing while normal orbiting satellites can only provide data each time they pass over an area. Remote sensing taken from cameras mounted on airplanes is often used to provide data for applications requiring more frequent sensing. Cloud cover can interfere with the data from a scheduled remotely sensed data system. Remote sensors located in fields or attached to agricultural equipment can provide the most frequent data. 2.2.1 SHORT HISTORY OF REMOTE SENSING TECHNOLOGY Remote sensing evolved during the First World War, when a bird eye view was badly needed for reconnaissance activities i.e. to ORFDWH WKH SRVLWLRQV RI HQHP\¶V covertly, to obtain information about the extent of ammunition and in general to identify opponents movements. After the world wars remote sensing technology was concentrated towards civilian activities i.e. obtaining information about natural resources for development, planning and management. The first known aerial photograph was taken in 1859 by Gaspard Felix Tournachon. He used a hot air balloon to obtain the photograph over Bievre, France. Balloon photography flourished after that photos from balloons were used by union troops during the American Civil War. The practice was later discontinued because balloons GLGQ¶WRSHUDWHFRYHUWO\DQGLW drew enemy fire. In 1903, The Bavarian Pigeons Corps in an attempt to avoid the expense and unpredictability of balloons and kites, the cameras were attached to pigeons. The birds were trained to fly in straight lines back to their home shelter. The cameras took photographs in thirty-second intervals. However many pigeons were shot down as they made their way home.   ϭϵ Image 2.2.1a Bavarian pigeon carrying a recon camera In 1908, for the first time planes were used to take aerial photographs. Air photographs became increasingly important for reconnaissance purposes throughout the last years of World War I. World War II brought a new era for aerial photography. Air photos for reconnaissance became extensively used. During this time, the development and use of different film capabilities increased. Infrared sensitive films was developed. Image 2.2.1b Aerial photography for reconnaissance activities   ϮϬ ,QWKH¶VWKH86EHJDQFROOHFWLQJLPDJHU\IURPVDWHOOLWHVIRUreconnaissance and intelligence purposes. Aerial photographs from Cuba led the US and Soviet Union into Cuban Missile Crisis of 1961.With each satellite launch, the technology and quality of satellite images increased. In 1972, NASA launched the first Earth Resources Technology Satellite (ERTS-1), which later became to be known as Landsat. Subsequent Landsat satellites were launched later. Today many remote sensing satellites are operational over the Earth and provide extensive data concerning the composition of our planet. 2.2.2 MILESTONES IN THE HISTORY OF REMOTE SENSING TECHNOLOGY 1800 The Discovery of infrared by Sir William Herschel 1839 The Beginning of practice of photography 1847 Infrared spectrum shown by A.H.L Fizeau and J.B.L. Foucault to share properties with visible light 1850-1860 Aerial photographing from balloons 1873 Development of the theory of electromagnetic energy by James Clerk Maxwell 1909 Aerial photographing from air planes 1910-1920 Aerial reconnaissance during the World War I 1920-1930 The Development and initial applications of aerial photography and photogrammetry 1930-40 Development of Radar technology in Germany, United States and United Kingdom 1940-1950 World war II: application of non-visible portions of electromagnetic spectrum; training of personel in acquisition and interpretation of aerial photos 1950-1960 Military research and development 1956 &ROGZHOO¶V research on crop disease detection with infrared photography 1960-)LUVWXVHRIWHUP³UHPRWHVHQVLQJ´   Ϯϭ 1972 Launching of Landsat 1 1970-1980 Rapid advances in digital image processing 1980-1990 Landsat 4: New generation of Landsat sensors 1986 SPOT-French Earth Observation Satellite ¶V'HYHORSPHQWRIK\SHUVSHFWUDOVHQVRUV March 24, 1998 Launching of SPOT4 Vegetation December 1999 Launching of VHR-Sensor 2.2.3 CAPTURING REMOTE SENSED IMAGES Survey of crops is feasible using infrared and colour cameras to detect the onset of disease through Changes in crop colour. Information from remote sensing are used as base maps for management of rust. The sunlight energy bounces off leaves and identified by human eyes as the green color of plants. A plant looks green because the chlorophyll in the leaves absorbs much of the energy in the visible wavelengths and the green color is reflected. Sunlight that is not reflected or absorbed is transmitted through the leaves to the ground. Interactions between reflected, absorbed, and transmitted energy can be detected by remote sensing.   ϮϮ Image 2.2.3 visible spectrum in a range of electromagnetic spectrum 2.2.4 THE TASK OF REMOTE SENSING TECHNOLOGY ON MAIZE RUST DISEASE (Puccinia sorghi) Remotely sensed images taken from drones, aircrafts or satellites provide a means to assess the development of maze rust disease in the field without FRQGXFWLQJ D FRQYHQWLRQDO ³VFRXWLQJ PHWKRG´ZKLFKLVXVed for years by agronomists. The normal scouting method is tiresome and not very much accurate. For Maize growers in Africa who most are small holder farmers, Maize rust is one of the biggest problem and unavoidable issue, which can ruin a whole crop if not managed meticulously and aggressively.   Ϯϯ RS technology enhances monitoring the development of circular to elongate pustules scattered over both surfaces of the leaf at early stages of infections. The Pustules which are powdery and brown in colour contain masses of spores (uredospores) tends to appear on any above-ground part of the plant. The overall appearances of plant which FDQGHWHUPLQHWKHSODQW¶VFRQGLWLRQ and the amount of infection caused by the disease. A drone is considerably affordable for some African farmers, and this can significantly help sound agronomic methods thus increasing returns per unit area of a farm and consequently ensuring ZRUOG¶VIRRGVHFXULW\  Ϯϰ  CHAPTER THREE 3.0 MAIZE RUST DISEASE 3.1 IDENTIFICATION OF THE DISEASE Maize rust disease (Puccinia sorghi) is fungal disease recognized by the appearance of circular to elongate pustules scattered over both surfaces of the leaf at early stages of infections. The Pustules which are powdery and brown in colour contain masses of spores (uredospores) tends to appear on any above-ground part of the plant, however they cause much infections on the leaves. As the time progresses the pustules tends to split exposing the spores, which are spread and disseminated by the wind to initiate and thus causing new infections. The disease is caused by a fungi known as Puccinia sorghi. Image 3.1 powdery and brownPustules When maize approaches maturity the colour of spores in pustules change from reddish to black due to formation of teliospores (resting spores). The disease infections are initiated from the leaf  Ϯϱ  margins and leaf apex then extend inwards to the leaf petiole. The disease is spread by air through the wind (Carrier) and deposited to new areas thus causing infections in other maize crops. The fungal infection causes black colorations on the leaves thus reducing the plant photosynthetic areas which in turn decrease food synthesis in the plant affecting its physiology and consequently reducing the yields. 3.2 DESCRIPTION OF MAIZE RUST DIISEASE Common rust is caused by the fungi Puccinia sorghi and is found in most subtropical, temperate, and highland environments with high humidity. Epidemics of common rust can cause substantial yield loss. Yield losses in excess of 50% have been recorded under severe disease pressure. 3.3 PATHOGEN CLASSIFICATION Kingdom: Fungi Phylum: Basidiomycota Class: Pucciniomycetes Order: Pucciniales Genus: Puccinia Species: Puccinia sorghi  Ϯϲ  3.4 SYMPTOMS OF MAIZE RUST Circular to elongate (0.2 to 2 mm long), with dark brown pustule (uredinia) scattered over both leaf surfaces giving the leaf a rusty appearance. Pustules may emerge in circular bands due to infection that occurred in the whorl. Pustules break through the leaf epidermis and release powdery reddish-brown spores (urediospores). As pustules mature tend release brownish-black spores (teliospores) which are the overwintering spores. Under severe disease pressure, leaves may turn chlorotic and senesce prematurely. Image 3.1 Release urediospores  Ϯϳ  3.5 CONFIRMATION OF THE DISEASE Symptoms of common rust are often hard to distinguish from those of Polysore rust. However, there are a number of subtle distinguishing features. 9 Appearance of pustules on both leaf surfaces. Pustules of Polysora rust emerge predominantly on the upper leaf surfaces. 9 Pustules of common rust are generally elongated and red to brown in color. Pustules of Polysora rust are more orangish and circular in appearance. 9 Common rust favors cool temperatures while Polysora rust favors high temperatures (above 24°C). Although both diseases thrive in humid conditions. 3.1 KEY DIFFERENCES BETWEEN COMMON AND POLYSORA RUSTS OF MAIZE. Common rust Puccinia sorghi Polysora rust Puccinia polysora Pustule appearance Elongated, scattered over the leaf Circular, evenly distributed over leaf Pustule color Dark red / brownish Orangish Both upper and lower surface of leaves. Generally only found on leaves. Predominantly upper leaf surfaces. Also found on stems and husks. Optimum environment Cool and humid conditions Warm and humid conditions ( above 240C). Distribution Subtropical and temperate regions. Tropical and subtropical regions. Occasionally in temperate regions if the temperatures are high enough. Causal agent Pustule location  Figure 3.1 The differences between common and polysora rusts of maize   Ϯϴ 3.6 LIFE CYCLE OF MAIZE RUST DISEASE The life cycle of P. sorghi involves two hosts (maize and Oxalis species) and five spore stages (teliospores, basidiospores, spermatia, aeciospores and urediospores). Urediospores can overwinter in tropical or subtropical regions and serve as the primary source of inoculum in subsequent seasons. They are disseminated by wind over long distances (hundreds of kilometers) and frequently spread from tropical/subtropical regions to temperate regions in spring and summer when maize is cultivated. The sexual stage of the life cycle occurs predominantly in tropical and subtropical regions. Teliospores are unable to overwinter in most temperate regions. Figure 3.2 life cycle of maize rust disease  Ϯϵ  3.7 HOST RANGE OF THE DISEASE As with most rust pathogens, P. sorghi has a complex life cycle and needs two unrelated hosts to complete its life cycle. The asexual stages of the life cycle are commonly complete on maize, while the sexual stage is completed on weeds Oxalis species (wood sorrel). Typically, the sexual stage is only completed in tropical region where asexual spores (uredospores) are wind disseminated to from temperate regions during the growing season. 3.8 MANAGEMENT ACTIONS OF COMMON RUST IN THE MAIZE FIELDS (a) Deep plough of crop residues, This will help to minimize disease spread by limiting disease dissemination and blocking the pathogen life cycle so preventing their success to next stage. (b) Destroy the weed Oxalis sp. (an alternate host), Weed the oxalis spp weeds which act as an altenate host to the fungi. The images captured by the drone and its Spectral signatures will appear different from those of the maize crop. Although weeds need to be controlled to reduce their impact on crop yield and quality, in the case of maize rust the weeds must be destroyed in reference to spectral signatures from the images captured to get rid of the pathogen shelters. (c) Timely fungicide application when pustules are first observed on the leaves, A drone flies a multispectral sensor that can identify the health vigor and density of the maize crop. Fungicide application to the affected crops must be done in reference to images captured to manage the disease strains in the farm. (d) Destruction of most affected plants, This will help to minimize fungal strains thus hindering spread by limiting disease dissemination and blocking the pathogen life cycle so preventing their success to next generation and overwintering. A drone flies a multispectral sensor that can detect infrared light, which gives an indication of the health vigor and density of the maize crop. Affected crops destruction must be done in reference to images captured to get rid of the disease strains in the field. (e) Cultivate early maturing varieties that limit secondary cycles of disease and can avoid periods of heavy disease pressure later in the season (f) Use of resistant maize varieties, Biological control through breeding for disease resistance or tolerance is the only feasible economic control. The diseases cannot be controlled economically by chemical means.   ϯϬ 3.9 THE EFFECTS OF MAIZE RUST DISEASE IN AFRICA: A CASE STUDY OF TANZANIA Maize (Zea mays) is the major cereal crop and staple food consumed in Tanzania. Its annual per capita consumption of maize in Tanzania is 112.5 kg while the national maize consumption is estimated to be three million tons per year. Maize is grown in all regions of Tanzania. The crop is grown on an average of two million hectares or about 45% of the cultivated area in Tanzania. However, most of the maize is produced in the Southern Highlands (46%), the Lake zone, and the Northern zone. Dar-es-Salaam, Lindi, Singida, Coast, and Kigoma are maize-deficit regions. For Maize growers in the southern highlands of Tanzania who most are small holder farmers, Maize rust is one of the biggest problem and unavoidable issue, which tends to ruin the whole crop if not managed aggressively. Most farmers grew maize as a major staple food and cash crop. Maize common rust is of economic importance because it causes yield loss of about 50%. As most of the maize is produced in the Southern Highlands which makes (46%), Southern highlands is the basket of the country. So when encountering such setback it also leads to deficient food supplies in the country consequently slowing down the country's economic development.  ϯϭ CHAPTER FOUR 4.0 DRONE OPERATIONS AND MECHANISMS IN THE FARM 4.1 DRONE TECHNOLOGY MECHANISM IN THE FARM For large and medium scale farmers or OHW¶VVD\IDUPHUVZLWK above 10 acres farm the ability to do a conventional agronomic scouting method on a daily basis is almost impossible. Its impossibility is attributed by crop density. But it is possible with a small drone prototype to combat rust. Image 4.1 Aerial photograph captured by a coloured drone camera The drone prototype is launched over the farm with an autopilot program designed to take it to specific coordinates. Also attached with sensors like an infrared camera which captures multispectral images of the farm. A computer program crunches the wavelengths in each pixel, making it possible to hone in on colors and temperatures ± and locate maize rust.  ϯϮ Survey of crop become feasible using infrared and colour cameras to detect the onset of disease through Changes in crop colour. Information from the drone prototype is used as base maps in making different agronomic decisions and especially where immediate actions should be taken to serve the maize crop. 4.2 DRONE PROTOTYPE TO COMBAT RUST IN MAIZE FIELDS Equipped with a high-performance global positioning system (GPS) combined with the attitude control system (ACS) and with an aid of an autopilot program form a system which will make the drone operation simple. High performance GPS increases the drone stability and directional control, increasing its safety and accuracy. The Drone prototype must be equipped with a unique control support system that performs the computer controlled movements to achieve maximum flight stability. The unique control support system (UCSS) allows easy and precise hovering mechanisms for control of the drone, so making it possible to perform highly efficient and effective operations as intended by the farmer. Altitude Control System (ACS) will monitor and maintain height, direction and speed. The drone shall also be the integrated with specialised sensors, such as multispectral and hyper spectral sensors, thermal cameras, and laser scanners, to map and monitor different aspects of the field at ultra-high resolutions. The concept focuses on the use of the drone for aerial surveys thus monitoring and managing the rust effectively. 4.3 MOUNTING THE DRONE OPERATION OVER MAIZE FIELDS Drones can be used just like you would a plane or a satellite, it is a mini satellite which carries sensors that are needed to study the farm conditions. The same idea can be used to combat maize rust in the fields, but now with low costs which are affordable to the farmer. The main goal is to   ϯϯ fly over the farms and map no matter how lush and dense the crops are. The drone is operated by an unmanned aerial vehicle (UAV) pilot where the drone can take off and land by itself. It can flies to a given height between 50-100 metres above the ground and then the autopilot takes over and it flies to GPS waypoints, it can fly between 5-10 minutes and in that time it can cover around two hectares. The main goal is to help farmers, agronomists and farm managers to better understand their maize crop health conditions giving a bird's- eye view (an acute eye vision). 4.4 MONITORING HEALTH OF MAIZE VEGETATION THROUGH THERMAL AND MULTI-SPECTRAL CAMERA The drone is used to examine the health of vegetation through thermal, multi-spectral and stillimage cameras attached to its undercarriage. It is deployed in the maize fields with an aid of an auto pilot where it flies to a given height between 50-100 metres above the ground and then the autopilot takes over and it flies to GPS waypoints. When reaching the coordinates, drone starts taking pictures at a farmer-specified setting. The differences in leaf colors, textures, shapes or even how the leaves are attached to plants, determine how much energy will be reflected, absorbed or transmitted. The relationship between reflected, absorbed and transmitted energy is used to determine spectral signatures of individual plants. Spectral signatures are unique to plant species. The images obtained will identify stressed areas in fields by first establishing the spectral signatures of healthy plants. The spectral altered from those of healthy plants. For Example, The comparison of spectral signatures of healthy and stressed sugar beets. Stressed sugar beets have a higher reflectance value in the visible region of the spectrum from 400-700 nm. This pattern is reversed for stressed sugar beets in the nonvisible   ϯϰ range from about 750-1200 nm. The visible pattern is repeated in the higher reflectance range from about 1300-2400 nm. Interpreting the reflectance values at various wavelengths of energy can be used to assess crop¶V health. The comparison of the reflectance values at different wavelengths, called a vegetative index, is commonly used to determine plant vigor. The most common vegetative index is the normalized difference vegetative index (NDVI). NDVI compares the reflectance values of the red and NIR regions of the electromagnetic spectrum. The NDVI value of each area on an image helps identify areas of varying levels of plant vigor within fields. For Example, The comparison of spectral signatures of healthy and stressed sugar beets. Stressed sugar beets have a higher reflectance value in the visible region of the spectrum from 400-700 nm. This pattern is reversed for stressed sugar beets in the nonvisible range from about 750-1200 nm. The visible pattern is repeated in the higher reflectance range from about 1300-2400 nm. Interpreting the reflectance values at various wavelengths of energy can be used to assess crop¶V health. The comparison of the reflectance values at different wavelengths, called a vegetative index, is commonly used to determine plant vigor. The most common vegetative index is the normalized difference vegetative index (NDVI). NDVI compares the reflectance values of the red and NIR regions of the electromagnetic spectrum. The NDVI value of each area on an image helps identify areas of varying levels of plant vigor within fields.   ϯϱ Image 4.4 A drone image showing vegetation type, condition and density 4.5 MONITORING OF WEEDS EMERGENCE IN THE FARM THROUGH THERMAL, MULTI-SPECTRAL CAMERA The differences in leaf colors, textures, shapes or even how the leaves are attached to plants, determine how much energy will be reflected, absorbed or transmitted. The relationship between reflected, absorbed and transmitted energy is used to determine spectral signatures of individual plants. Spectral signatures are unique to plant species. The images obtained will identify weeds presence in the fields. Spectral signatures of the crop plants will appear different from those of the weeds.  ϯϲ  The weeds Oxalis species are the alternate host to the fungi causing common maize rust in the maize fields, the images obtained by the drone will portray Spectral signatures that will appear different from those of the maize crop. During drone operations the use of unmanned aircraft systems (UAS) and the integration of small sensors for environmental remote sensing and aerial surveys are put into action where by the drone works together with other components as a system. The UAS and the integration of specialised earth observation sensors, such as multispectral and hyper spectral sensors, thermal cameras, and laser scanners are used to map and monitor different aspects of the farm at ultra-high resolutions. A drone flies a multispectral sensor that can detect infrared light, which gives an indication of density of the maize crop. The drone can fly between 5-10 minutes and in that time it can cover an area of around two hectares of the maize fields substituting the work which could be done by three people, in a short period of time and much more accurately. Farm managers and agronomists can use these maps to make decisions about where the rusts has developed and subsequent solution. For large scale farmers in Europe they used to hire airplanes which is attached with cameras and other sensors to detect the field conditions. The method is quite expensive for small holder farmers in Africa with an average acreage area of about two acres. +RZHYHU QRZDGD\V LW¶V DOO DERXW D SRUWDEOH 8$9 V\VWHP ZKLFK GRHV QRW FRVW PRUHWKDQ  US$. The farmers in Europe and across North America are in the progress of adopting such technology but it has never been that easy because there is much in research to be done in order to intergrate this technology into the farms. As it was initially for military reasons. The technology allows small holder farmers in Africa to increase their yields because it allows early assessment of the field crop conditions and providing answers instantaneously with low costs.  ϯϳ  CHAPTER FIVE 5.0 CHALLENGES OF ADOPTING PRECISION AGRICULTURE TECHNOLOY FOR AFRICAN AGRICULTURE INDUSTRY 5.1 OPPORTUNITIES FOR AFRICAN AGRICULTURE INDUSTRY $JULFXOWXUHKDVEHHQGHVFULEHGDV³$IULFD¶V6OHHSLQJ*LDQW´$:RUOG%DQNUHSRUWRIWKDWQDPH argues that there are great opportunities for African farmers, especially in the light of projected stronger demand in world markets for agricultural commodities in the long term. Far from being a poor and barren region, Africa has major advantages in terms of climate, land, water, natural and human reVRXUFHV7KHNH\WRZDNLQJWKH³6OHHSLQJ*LDQW´LVPRGHUQWHFKQRORJ\ Continued population and income growth, combined with increased urbanization particularly in developing countries, are placing pressure on current global food supplies. With its great natural potential, Africa can evolve as a major food exporter for other regions of the world and enjoy higher economic development through trade. Indeed many experts consider Africa as a major source of future supply and stability for food and industrial agricultural markets, given its extensive, uncultivated land resources and unrealized potential productivity gains. The value of the African agricultural industry could reach approximately 1,000 billion USD ($ 1 trillion) by 2030 provided that infrastructure and irrigation systems improve, farming techniques are intensified, farming technologies are modernized and better political and economic stability is achieved. Advanced Precision Agriculture (PA) techniques using GNSS are expected to play a major role in improving productivity levels in Africa.  ϯϴ  5.2 CHALLENGES FOR AFRICAN AGRICULTURE INDUSTRY IN ADAPTION OF RS AND GIS TECHNOLOGY Generally, the agricultural industry in Africa is underdeveloped with the exception of a number of countries that use PA technology i.e. South Africa and North African countries. In South Africa, wheat farmers use GPS for tractor auto guidance and for Variable Rate Applications (VRA) of lime, pesticides and fertilizers. The main problems and limitations for acquiring of PA, and hence GNSS, in African agriculture industry are the following: 9 Low resource base, Farmers do not have enough disposable income to invest in advanced precision agriculture equipment and other advanced agri-technologies. 9 Land fragmentation, The majority of farmers engage in small scale, subsistence farming which leads to land fragmentation. This leads to investment in GNSS and GIS technologies economically unviable. For example, it is estimated that 80% of all farms in Sub-Saharan Africa are smaller than 2 ha. The land fragmentation phenomenon is further intensified when family farms are inherited by PXOWLSOHIDPLO\PHPEHUVDIWHUWKHRZQHU¶VGHDWK 9 Low Mechanisation of farming techniques, In practice this means that there are not enough tractors in most countries to be equipped with GNSS. The difference in mechanization is marked between Northern and Sub-Saharan Africa,  ϯϵ  with the former having an average of 143 tractors per 100 square km and the latter only 15 tractors per 100 square km, numbers that have not changed much over the years. 9 No access to financial services and credits, Most of African IDUPHUVGRQ¶WNHHSUHFRUGVWKLVZLOOOHDGWRLQFUHDVHGGLIILFXOWLHVWRDFFHVV loans. Thus productive investment is hindered due to failure in financing some parts of the value chain. 5.3 OPPORTUNITIES FOR GNSS IN AFRICA GNSS uptake in Africa is expected to occur by the extension of EGNOS from Europe. Currently the three EGNOS satellites cover the entirety of both continents, and ground stations are based in Europe and Northern Africa. Therefore EGNOS could extend its services to cover Africa by adjusting the current system and constructing ground stations, which link to the European network or to an equivalent African SBAS. In South Africa, for instance, farmers have already equipped their tractors with EGNOS-enabled precision equipment and the future extension of EGNOS to Africa is expected to lead to a significant increase of PA technologies usage. In countries adjacent to South Africa, governments are making huge areas of land available to farmers from South Africa to manage. As a result, South African companies are starting to develop sugarcane and maize crops on big commercial farms, which are ideal new markets for the application of PA GNSS technologies. Lots of potential also exist in fairly basic PA tools (in the context of Europe and North America).  ϰϬ  5.4 THE USE OF HAND-HELD DEVICES IN AGRICULTURE Smartphone penetration is booming in Africa, currently they are used for carrying out various activities that are otherwise restricted through lack of infrastructure (e.g. financial transactions, online payments). Currently Africa is far advanced compared to other parts of the world when to it comes to electronic-smartphone aided money transfer, with Tanzania and Kenya being the top leaders, following the same path we can do it in introducing precision farming and GNSS applications and thus awakening the sleeping giant. Another way of fostering GNSS uptake in Africa, which does not involve equipping tractors or machinery with receivers, is the. In combination with yield mapping and related geo-information, handheld devices could have a very significant impact on productivity of African farms. For example, farmers could receive average yield maps on their smartphones or tablets through the internet and use them as guidelines for applying fertilizers and pesticides, moving away from rough estimates, saving costs and improving margins.  ϰϭ  5.4 SUPPLEMENTARY ACTIVITIES TO FOSTER AGRICULTURE DEVELOPMENT Although increased use of GNSS is expected to improve the productivity of African farming, additional actions are necessary to ensure that the appropriate environment is created for smallholders to invest in PA systems and lower their exposure to risks. Governments, NGOs and investors should be encouraged to direct their attention and funds to: 9 Development of strong farmer cooperatives union, Assist the formation of IDUPHU¶V cooperatives and transition to farming as a business by facilitating VPDOOKROGHUV¶SDUWLFLSDWLRQLQPRUHintegrated supply chains, which are generally characterized by higher returns and lower levels of risk. 9 Providing financial education, This will IDFLOLWDWHIDUPHUV¶DFFHVVWRPDUNHWV and credits and show them which parts off the value chain should be properly financed and thus increasing returns per unit lad area. 9 Build adequate storage facilities and processing units WRIDUPHU¶VOHYHO, Good storage facilities and processing units will help to minimize post-harvest losses. For example in Tanzania currently only 10% of fruits and vegetables are processed, others are lost as postharvest loss. 9 6WDELOL]HPDUNHWVDQGUHGXFLQJSULFHV¶DQGLQSXWand farm tools FRVWV¶YRODWLOLW\ 9 Invest in agricultural R&D in research centers and African universities. 9 Providing agriculture loans, This should involve inputs, tools and liquid money loans at low interest rate. Loans must also be provided to all members of the society who have the interest in farming without exceptions.   ϰϮ Currently FINTRAC through the USAID is doing some good work but there is a necessity of other contractors and donors to intervene   ϰϯ CHAPTER SIX 6.0 DRONE TECHNOLOGY FUTURE AND PRIVACY ISSUES 6.1 UN MANNED AIRCRAFT VEHICLE SYSTEMS (UAVS) AND PRIVACY There has been a lot of negative news about the use drones being dangerous and that they invade privacy. The uses of UAV technologies raise a broad range of issues that relate to collection, retention, use, disclosure, and eventual safe destruction of personal information. The potential for institutional or other abuse, arising as a result of the inappropriate use of these technologies, suggests a need for safeguards tailored to prevent intrusions into privacy and liberty. Whether sensor enhanced UAVs are used by government agencies, commercial entities, or small personal entities or whether model aircraft are used by private individuals for recreational purposes privacy issues must be addressed. UAVs present unique privacy challenges, due to the manner in which they may collect information. While some of the sensor equipment on board UAVs may be commonplace in the consumer electronics marketplace, the ability to gather information dynamically from unique vantage points would appear to distinguish UAV use from other video surveillance cameras, and from data collected using cell phone technology. Rather than taking a privacy compliance approach to system design, organizations should take a proactive approach to developing and operating a UAV program which respects privacy. This will ensure that the proposed design and operation of the UAV system limits privacy intrusion, if any, to that which is absolutely necessary to achieve required, lawful goals.   ϰϰ 6.2 UN MANNED AIRCRAFT VEHICLE SYSTEMS (UAVS) FUTURE To ensure drone privacy policy and future, it is worthwhile to consider recent developments, beginning with U.S. and Japan context which appear that the regulatory authorities of the two countries are more helpful than in those of some other countries. In Japan UAVS are regulated by the Japan Agriculture Aviation Association (JAAA), who regard the system as agricultural equipment, and it is certified as such for reliability in operation. The Association require that the distance between the unmanned vehicle and its operator not to exceed 150 m. The current rule in the UK, however, asserts that the UAV must stay within the sight of the operator. To do so totally removes the economic advantage of the UAV operation and so prevents the use of that RSHUDWLRQDVDµEXLOGLQJEORFN¶IRUIXWXUH8$6DSSOLFDWLons. Public concern with the prospect of unmanned aircraft flying around the skies and possibly crashing onto people and property or colliding with other aircraft is perfectly understandable. It is necessary that regulations are in place to prevent cavalier and ill-considered use of non-airworthy and unreliable systems causing loss of life and damage to property. This is in the interest of responsible manufacturers and users of UAS who would wish to see public confidence building in the responsible deployment of well-conceived systems to the economic and environmental advantage of all.   ϰϱ 6.3 CONCLUSSION AND RECOMMENDATIONS 6.3.1 CONCLUSSION The main goal is to help farmers and especially smallholder farmers in Africa to be competitive on the market in the sense of required products, quality and amount. This will enable them to counteract changes in the market, changes in subsidies systems, environment destruction, soil fertility depletion and climate change. 6.3.2 RECOMMENDATIONS One of the major constraints from farm yields increase in Africa is soil fertility depletion resulted from continuous agricultural activities over the years done without supplementing the absorbed nutrients. When synchronized together with other activities like formation of farmer associations, groups and cooperatives so sharing equipments and tools like machines, workers especially trained experts OLNHH[WHQVLRQLVWV$JURQRPLVWVHQJLQHHUVHWF7KHWHFKQRORJ\FDQKHOSWRHQVXUHWKHZRUOG¶VIRRG security by developing small holder farmers from hand to mouth yields into developed farmer with surplus productions. As main stake holders of the farming industry small holder farmers must have the perspective which respects changing conditions and demands. This will enable them to be flexible to other new technologies like Genetic Modified Organisms (GMOs), Integrated Pest Management (IPM) Organic farming (OM) and Precision agriculture (P.A).   ϰϲ Different stakeholders must be involved to ensure successful precision agriculture technology in the farming industry. These will include from the Government through the Ministry of Agriculture food security and cooperatives to provide subsidies, education and experts, individual farmers who must possess a sense of development and agrarian revolution for the global future. There must be a complete and continuous chain from national level to the individual farmer level to achieve the goals of the successful farming industry.  ϰϳ 7.0 REFERENCES Adamchuk, V.I., Perk, R.L., & Schepers, J.S. (2003). Applications of Remote Sensing in SiteSpecific Management. University of Nebraska Cooperative Extension Publication EC 03-702. Bauer, M.E. (1985). Spectral inputs to crop identification and condition assessment. Proceedings of the IEEE, 73, No. 6, 1985, 1081. 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