case study to investigate the adoption of precision

Case Study to Investigate the Adoption of Precision Agriculture in Nigeria Using Simple Analysis to Determine Variability on a Maize Plantation

AJAERD


*Halimatu Sadiyah Abdullahi1, Ray E. Sheriff2

1,2Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom

This study investigated the adoption of precision farming (PF) technology with research into the possible implementation of the technology for increased productivity in a maize plantation in Nigeria. The research understands the nature of the challenges and highlights the possibility of implementing PF technology to Nigerian Agriculture. The methodology uses simple image analysis with fuzzy classification to determine the degree of spatial and temporal variability of the field to develop a treatment plan for an equally fertile and fully productive yield. The results showed that implementing precision agriculture (PA) will yield high productivity with the aid of remote sensing to obtain an aerial view of the farm. Simple PA technologies, such as using the information to determine and test soil nutrient availability to enable land preparation to obtain a uniform field, can help make the managerial decision on the farm efficiently. There is a great chance to optimize production on the field, minimise input resources, cost and maximising profit while preserving the natural environment. By using machine vision technology with fuzzy logic for decision making, not only the shape, size, colour, and texture of objects can be recognised but also numerical attributes of the objects or scene being imaged.

Keywords: Precision Agriculture, classification technique, feature extraction, Image analysis, Decision making, variability.

INTRODUCTION

With the potential of the space agency in Nigeria, aerial surveillance for constantly monitoring an agricultural plantation using satellites and other remote sensing technologies has improved and made possible the adoption of precision agriculture for the optimized production of food (Valente et al., 2011). The National Space Research and Development Agency in Nigeria, for example, has successfully launched five (5) satellites and are planning to launch more to replace others reaching their estimated life-cycle (Nasrda, 2008). These Nigerian satellites have contributed in addressing some the nation’s challenges in areas like the recent flooding(Nema, 2014) in providing: early warning signs and provision of contingency plans; and images of the Sambisa forest, where the kidnapped Chibok girls were believed to be held (Adams, 2014). Also, Nigerian SAT 1 was part of the first satellite to return pictures of the east coast of the United States following the Hurricane Katrina and has provided some images for mapping and development of certain areas (Paul Osas, 2013).

Research into the agricultural sector shows that satellite services are very important and yet to be explored in Nigeria (Unoosa, 2016) (Asian Development Bank, 2014) (Meera, Jhamtani, and Rao, 2004) (Vergragt, 2006). With these potential, the use of remote sensing can be explored to monitor agricultural plantation, detect early onset of the effect of pests and diseases, determine the harvest period, prepare soil before planting to ensure maximum production with minimal losses of products, minimize losses on the field by providing exact harvest dates, reduction in addition to input resources and also deliver the right amount of nutrient resources on the field (Mengistu and Salami, 2007).

*Corresponding author: Halimatu Sadiyah Abdullahi, Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom. E-mail: [email protected]

Journal of Agricultural Economics and Rural Development Vol. 3(3), pp. 279-292, November, 2017. © www.premierpublishers.org. ISSN: XXXX-XXXX

Review Article


Abdullahi and Sheriff 280 The Nigerian economy solely depends on oil for the provision of its resources, funds, payment of wages and in general, the Country’s running costs. This contributes less than 40% of GDP despite its dependence and has created a lot of problems with unemployment, and an uneven or unequal distribution of wealth among a certain small percentage of individuals, leading to extreme poverty in the hands of the majority (Otiotio, 2015). Individuals with access to oil had automatic tickets to becoming rich within a short period without much hard work required, this has a negative effect and leads to discouragement in the Agricultural sector previously one of the major sources of revenue (70% GDP) generation before the oil boom in 1970 (Pettinger, 2015a). Recently, due to the shortfall in the price of crude oil(Pettinger, 2015b), the agricultural sector is the other sector that is being explored to bring a long lasting solution to the economic situation (extreme poverty, unemployment, starvation etc.) by providing increase in GDP and leading to more job opportunities creation with abundant food supply. The Strength, Weakness, Opportunities, Threats (SWOT) analysis were carried out on the Nigerian economy to justify the need for the research. Figure 1 shows the SWOT analysis for agricultural development in Nigeria. Technology application to agricultural development is fast increasing and broadly practised in so several parts of the world where agriculture is as a major source of income and livelihood (Diouf et al., 2002). Technology int his contaxt refers to the application of scientific knowledge for practical drives or the use of machinery to enhance, facilitate a process and reduce the rigorous manual labour involved in agricultural production (Rada and Valdes, 2012). These applications to the agricultural sector helps to eliminate the stress and hectic manual intensive labour involved in agriculture. It also tends to optimize yield and aids proper management of farm input resources translating into output (Wei and Balasubramanyam, 2015). In Nigeria, food security is at the heart of economic and social development priorities, which will lead to political and economic stability in the country by making more food available, improving its quality and making it readily accessible to more people (Steer, 2008). This will serve to bridge the very wide gap between the rich and poor.

The technological development in agriculture serves to lessen the human labour involved in farming. Technology is playing a very significant role in the primary, secondary and even in (tertiary) marketing these days which is critical to the agricultural-industry companies (Lopes, 2010) (Dr. William C. Motes, 2010). It is only with large production being aided by the available recent technology and exhaustive marketing that the farmer exploits both the local market and the global market to its full extent (Product, 2008).

The application of technology to agricultural production is broadly classified into two (2) broad sub-mains namely: yield technology and the ICT technology. Figure 2 shows the classification as well as other aspects required for better management and improvement of the agricultural sector.

Yield technology is commonly referred to as the technology that aids the growth of the plantation from pre-planting to post-harvest, while ICT refers to the services that help with the implementation of the technology use like the software, internet facility and communications tools. With technology, Agriculture or farming is fast translating to a more and more information-based industry in the reaction to an economic and environmental considerations to help meet the requirements for the observational data obtained with the use of the aircraft, UAV, balloons and satellite through remote sensing which is playing a long-drawn-out role in farm management through precision agriculture(Deere, 2007). Figure 3 shows the role played by each of the technologies as highlighted.

The system requirements as shown in figure 3 refers to data management and processing involving the overall process of data acquisition, data analysis and dissemination of the data for use by the farmers. This is all about obtaining relevant data and ensuring its delivery at the right time and to the right source. This technology is referred to as Precision Agriculture (PA) (Pet al., 2011). LITERATURE REVIEW To study and understand the nature of Nigerian Agriculture, the total mass of land available to agriculture with the percentage of arable land is represented below according to World Bank indicators (The World Bank, 2016) is represented in table 1.


J. Agric. Econ. Rural Devel. 281

Figure 1: SWOT Analysis for Agricultural Development in Nigeria


Abdullahi and Sheriff 282

Figure 2: Classification of Agricultural Technology

Figure 3: Impact of Technology on Agriculture.


J. Agric. Econ. Rural Devel. 283 TABLE I: World Bank indicators of the percentage of arable land in Nigeria

World Bank Indicators

Year 1990 2000 2005 2010 2012

Agricultural acreage (sq. km) in Nigeria 720740 718500 762000 742000 760000

Agricultural land (%) of land area available in Nigeria 79.11 78.90 84.0 81.8 83.7

Arable land inhectares in Nigeria 29539000 30000000 34200000 34000000 36000000

Arable land (hectares per person) in Nigeria 0.30 0.20 0.25 0.22 0.22

Arable land (%) of land area in Nigeria 32.4 32.9 38.7 37.0 39.2

Permanent yieldland (%) of land area available in Nigeria 2.8 2.9 3.3 3.3 3.5

Forest area (sq. km) in Nigeria 172340.0 131370.0 110000.0 90410.0 70435.0

Forest area (% of land area) in Nigeria 18.9 14.4 12.20 9.9 9.5

From table 1, is evident that in Nigeria, there is a huge problem of misuse of agricultural land (Advameg, 2010) that can be averted by making it necessary to adopt the techniques of remote sensing processes, Geographical Information system (GIS) and Global Positioning system (GPS) technology. This process allows the acquisition of real-time data and facts which can then be used in managerial decision-making process on the plantation. All of these processes are centred on PF. PF is a technology that influences the whole production process from extension services to managerial functions on the farm. It is largely a data technology based farm management framework, and it includes a process of data/image collection, data and information mapping, data analysis and location-specific treatment (Abdullahi, Mahieddine, and Sheriff, 2015). Precision agriculture provides information about the nutrient content and soil quality available across a particular field or plantation. PF technology which also includes farm product mapping, and the variable rate of nutrient application, can significantly increase the effectiveness of the required farm operations. Precision agriculture (PA) is majorly concerned with two simple technologies: GIS and GPS technology utilising different kinds of sensors, displays, and controllers for guiding farm’s equipment during operation (Cox, 2002). The components of PA are Spatial referring, plants and soil monitoring, Decision support system and Differential action systems. The cycle of PF comprises taking images to analyse for creating map yields, weeds,before the application of the input resources like herbicides, fertilizer, water to obtaining results for carrying out theoperations (Deavis, Baillie and Schmidt, 2009)(Goddard T, 1997). Precision agriculture revolves around data analysis and evaluation with its use in precision soil preparation, precision seeding, precision crop management and precision harvesting. Figure 5 shows all details (opportunities, technologies, processes) involved with precision agriculture (Horne, 2014). Prior to an agriculturist starting the process of PF, a smart idea and comprehension of the soil types, hydrology, microclimates and aerial photography of the farm sites are required (Mulla, 2013), and also as an understanding of

the constantly changing factors in the fields that affects the yield map. The yield map is a confirmation of data of what tis available to the farmers, by simply taking an aerial images of the farm in consideration. Remote sensing used in realizing the technology have platforms with satellites, aircraft, balloons and helicopters, with a variety of sensors like the optical, near-infrared sensors and RADAR (Radio Detection and Ranging) fitted on these platforms for its uses. Analytical information obtained from the images dowloaded from these onboard sensors, like the biomass, Leaf Area Index (LAI), disease, water stress and lodging, can now effectively assist in crop management, yield prediction, and environmental protection and safety (Zhang and Kovacs, 2012)(Haboudane et al., 2004). The cycle of precision agriculture application is shown in figure 4 as discussed above. The cycle of obtaining high resolution images involves the use of a low-cost multi hi-resolution imagery sensor mounted on a mobile ground receiving station and analysis centre with internet based georeferencing and GIS processing (Sharma, 2007). The electromagnetic (EM) scans are used in identifying the different soil types, and the layer of images or data obtained are used to create variable rate seed maps for improving crop production. Farm equipment like tractors use auto-steer systems with the information available. The sprayers attached to the tractors also have an auto section control and auto boom height for regulation during operation (Rickatson, 2012). PA has successfully been used since 2007, specifically after yield maps and target soil sampling have been in use. They are used to create instruction take-off maps for farm input nutrients like phosphate and potash to apply to crops, while the Yara-N sensors are used in applying a variable rate of nitrogen to the farm (University of Nebraska–Lincoln, 2015). No Current study is available presently that shows the process of adopting and using PA in Nigeria but has been studied in several parts of the country where PA Agriculture has been adopted like in the USA, Brazil, Russia, South Africa by different researchers. Most people argue that adopting PA costs a lot but research has shown that PA effects cuts across a long period with an increase in production and savings in resources over the period. A research conducted to determine how farmers are willing

http://www.tradingeconomics.com/nigeria/agricultural-land-sq-km-wb-data.html

http://www.tradingeconomics.com/nigeria/agricultural-land-percent-of-land-area-wb-data.html

http://www.tradingeconomics.com/nigeria/arable-land-hectares-wb-data.html

http://www.tradingeconomics.com/nigeria/arable-land-hectares-per-person-wb-data.html

http://www.tradingeconomics.com/nigeria/arable-land-percent-of-land-area-wb-data.html

http://www.tradingeconomics.com/nigeria/permanent-cropland-percent-of-land-area-wb-data.html

http://www.tradingeconomics.com/nigeria/forest-area-sq-km-wb-data.html

http://www.tradingeconomics.com/nigeria/forest-area-percent-of-land-area-wb-data.html



Figure 1: The Cycle of Precision Agriculture

Figure 5: All about Precision Agriculture to adopt the technology showed that Forty-three percent of the participants made profits from using precision services, thirty percent thought they broke even from covering only the fixed and variable costs of proposing the services in Australia. Researchers continue to expect growth in the practise of PA, and this growth is reported to be very substantial in the Midwest when compared with other states. Griffin et al. (2004) did a research on data on the adoption of PA technology in the USA, and according to the research, about ninety percent of the world’s yield monitors are there (Maré and Maré, 2002).

Other research conducted in Brazil to determine the extent to which PA technologies are used and the advantages and challenges encountered was investigated on a sugar-ethanol industry in Sao Paulo that produces sixty percent of the country’s domestic sugarcane. They are adopting PA and have recorded a seventy- eight percent increase in yield and also had findings that the major problems hindering the adoption of the technology are the high costs of services, technologies and lack of skilled staff to implement the technology (Silva, de Moraes and Molin, 2011).

https://link.springer.com/article/10.1007/s11119-009-9155-8#CR5


J. Agric. Econ. Rural Devel. 285 Also, Robertson et al successfully practised precision agricultural technology using variable rate and yield mapping which on a plantation in Australia and resulted to a significant increase in yield. Walton et al also used the same technology to sample soils before planting in the USA to ensure equal distribution of soil nutrients for an optimized yield (Silva, de Moraes and Molin, 2011).

Generally, PA brings about individual gains and with the auto steer systems on the tractors, gives the best advantages with its production efficiency with the operator the opportunity to concentrate on monitoring the job rather than driving. This means that the grid lines to be followed on the plantation are accurately established and all subsequent passes will be accurate. Variable rate application (VRT) of fertiliser on a farm land has allowed some savings on the farm with the fertiliser applied to the right areas of the field in the right amount serving as an advantage to the economic and environment (Joint Research Centre (JRC) of the European Commission, 2014).

A challenge usually encountered with PA is managing and analysing the very large data sets which are currently saved on cloud systems and to transfer data from one device or equipment to the other, or mobile devices. Another feature of PA being used is for weed spotting and site-specific in-crop treatments that have been efficiently used for weeds(Goddard T, 1997). PF, also helps to monitor the vegetation’s physical and chemical parameters by placing electrical conductivity, temperature, nitrates, soil moisture, evapo-transpiration and radiation sensors (Zhang and Kovacs, 2012). They ensure that the optimal conditions for plant growth is achieved. The DSS proposes the best time for watering and in the required quantity the need to overwhelm the salt substance because of abundance in the radicular zone, the necessity to prepare etc. Presenting a PA framework in an everyday operation of an agrarian misuse, time is saved because of the up-to-date estimation systems. Information obtained from the sensors are transmitted to a central database server, and this can be viewed by using a Smartphone or Laptop. Alternatively, email or SMS alerts can be modified to convey to the agriculturist when there is a need to flood, prepare or address issues in their properties. Also, costs regarding water, pesticides, and other farm resources are improved and can unquestionably be decreased significantly (Rajagopalan and Sarkar, 2008). . MATERIALS AND METHODS

The study was conducted on five farms with different locations in Nigeria. The study sites included: a rice plantation; vegetable; groundnut; melon and a maize plantation located in Abuja and Kwara state (llorin) (River Niger basin) of Nigeria, but with more focus on the rice and maize plantation, as these two crops are widely consumed food, preserved for long-term use and eaten in different forms.

Images of the maize plantation were taken and an interview guide was set up for the farmers on site to answer some questions to determine their willingness and opportunities to adopt the technology. Table 2 summarises the responses obtained from transcribing the data from the audio/video recordings. In the survey, questions were designed to inquire about the level of technological advancement in the farm practices and the immediate threat farmers face. The data obtained were the level of education of the farmer/farm representative, production characteristics, the size of farmland, current adoption of the technologies, land characteristics, etc. Technologies that were considered include the variable rate application (VRT) of nutrients, variable rate application of seeds, field inspection for insects, pests and diseases, etc. The aim here is to understand the immediate threat or challenges faced by Nigerian Agriculture and find out if precision agriculture will be easily adopted as a solution to the challenges. The pie chart in Figure 6 shows the distribution of farmland used in this survey.

Figure 6: Pie chart to show land distribution of farm sites

Judging from the data gathered from the plantation, there is a low level of technology usage on the farms due to Lack of funds or cooperative facility to farmers; inadequate education/extension workers and Lack of availability pf the required technology. Other common challenges were; Inadequate recording keeping making it difficult to determine profit and losses; constant flooding from water in dams overflowing to plantations especially in the rice plantation; Influence of early onset of pests and diseases; inadequate storage facilities; Lack of access to market due to bad roads and poor storage facilities; high cost of


Abdullahi and Sheriff 286 Table II: Transcribed Nigerian Farm data obtained from Questionnaires General Information Public Private Private Private Private

Farm ownership Types Government Private Company Family Private Company Private Company

Farm location Ilorin Abuja Ilorin Niger Ilorin

Date of farm visit June 8th, 2016 June 15th, 2016 June 16th, 2016 July 18th, 2016 July 20th, 2016

Farm size 100 hectares 70 hectares 34 hectares 10 hectares 200 hectares

Plantation type Maize intercropped with groundnut

Rice plantation Vegetables (spinach)

Watermelon Rice

Season of planting Rainy season Rainy season Rainy season Rainy season Rainy season

Type of water use (irrigation/rainfall)

Rainfall Rainfall dependent

Rainfall dependent

Rainfall dependent Rainfall dependent

Use of fertilisers, manure Extensive use Extensive use Used heavily as a source of nutrient

Applied twice during growth period

Extensive use in plantation

Type of farming practised Use of a few machinery provided by the government

Use of machinery rented

Subsistence farming

Use of machinery obtained with personal funds

Use of machinery for land preparation obtained with personal loan

Maximum yield produce No record No record No record Record of harvests, purchases kept

Predicted that output certainly outweighs the input

Type of Seeds planted GMO seeds GMO Seeds Best Seeds from previous harvests

Seeds from harvested previous plantation

GMO seeds

Soil assessment none No prior soil assessment

Soil preparation by tilling but with no soil assessment

Lab soil sampling with application of gypsum

Pre-processing and land preparation before planting.

Animal management None None None None none

Tools used in the farm/ machinery

Tractors, Ploughs, Harrows, thresholding machinery

Tractors, Ploughs, Harrows, thresholding machinery

Traditional tools. Tractors, Ploughs, Harrows, thresholding machine, sorter, grader

Tractors, Ploughs, Harrows, thresholding machine, sorter, grader

Maintenance of equipment’s

No routine maintenance

Yearly routine maintenance

Quarterly routine maintenance of equipment



Transportation of products Within the region of cultivation

Sold within the region where product is cultivated

Sold within 10 miles from harvest

Sold within the area of harvest

Some products are packaged and sent out for sale to other locations while some are sold at the local site of harvest.

Farmer’s standard of living/ health

Below standard: most farmers barely get enough products to sell or feed their family or educate their family.

Very low standard of farmers

Barely make enough to feed their family

Farm owners make a decent living from their harvest, but the farmers don’t have enough.

Farm owners make a decent living from their harvest, but the farmers don’t have enough.

Accessibility to data The Internet and local record keeping of some farms

Internet No previous data survey used.

Crowdsourcing/Internet

Internet/survey

Pest management Direct and equal application across fields

Direct and equal application across fields




Scouting for pests and diseases

Manually Normal detection with eyes

Detection with eyes and discolouration in plants

Manually Manually

Farmer Education level The majority of local farmers are uneducated with no prior knowledge of agriculture. Few educated farmers

Majority of local farmers are uneducated with no prior knowledge of agriculture




Years of experience in farming

10 25 40 28 13

Farmers employment status

Part-time employment with a government firm

Full-time farmers Full-time farmers Full-time farmers Full-time farmers

Age (farmers age (years) 46 58 47 65 53


J. Agric. Econ. Rural Devel. 287 traditional sampling making it difficult to determine the appropriate quantity of farm inputs required. All of the challenges are easily attirubtable to lack of adequate policies governing the practise of Agruiculture in Nigeria to control and minimize them but our major concern is addressing the problem of non-uniformity and intra and inter-field variability on the field leading to poor production Pictures of the maize plantation were taken alongside the interview with a high-resolution camera (Samsung Galaxy S6 edge+, model number; SM-G928F) A simple colour image of the maize plants acquired by a high pixel digital camera was selected to develop a cheap solution for predicting the nitrogen content of a leaf using its electromagnetic reflectance. In the colour image, it is not possible to distinguish the plant from soil because it has only the visible RGB spectra, which cannot distinguish between the reflectance of plant and soil. But with the use of a constant black background, a pure image can be separated from the soil image. The spectral bands for chlorophyll estimation are based on both the NIR in the 700 nm range and the green region in about 500 to 550 nm. With the RGB camera, plants usually absorb the larger part of the red and blue colour and do not absorb anything in the NIR region, but with a multispectral or hyperspectral camera, the plant reflects more of the NIR spectrum and absorbs the red spectrum accordingly. RESULTS AND DISCUSSION Based on this principle (Red and NIR spectrum absorption and reflection), sensors were built to accurately match this specification for remote sensing in those regions.

Comparison of the reflectance and the absorbance at the two different wavelengths, a green vegetation is obtained. NDVI correlates with photosynthetic efficiency, leaf area index and plant biomass, all of which can predict agricultural productivity, increase yield and prevent pollution (Labus et al., 2002).The use of satellite sensors have more advantages of covering a larger field to detect variability. The green band 520–600 nm, red 630 nm, and Near Infrared Bands (NIR) bands 760–900 nm are key for identifying and differentiating water and Nitrogen deficiency in both the visible and the near infra-red bands. Ground pictures clearly show a variation on the field making the plantations good for the applications of PF technology. A few ground control points were placed to capture the exact location and coordinates of the part of the field where the data was obtained. The resolution of the images would be better with the use of hyperspectral and multispectral sensors using UAVs as a sensor hook. UAVs are the latest technology used to obtain and in analysing images on the farm. The UAVs take the aerial view of the farms with high resolution making it possible to assess the condition of the plantation, reveal the state of health of the plant, plant/soil moisture, soil composition, flowering stage, pest infestation, etc. The analysis illustrates the significance and importance of geo-informatics technique to make a strong case for the adoption of the PF technology in Nigeria with the high variability observed on the field. Some of the images from the camera were stitched together for the analysis using auto stitch works and SIFT algorithm as seen in Figure 7.

Figure 7: Auto-stitched images of the maize plantation: (a,) healthy, (b) unhealthy plants


Abdullahi and Sheriff 288 Analysis 1: Analysis using simple colour image using fuzzy classifier The images were analysed using single image analysis algorithms in Matlab with visual techniques, and direct estimation using fuzzy classification. With the maize plantation, the colour effect shows the chlorophyll content, while the height of the plantation determines its rate of growth. A 3D model taking measurements of the plantation over a period will estimate the rate of growth of the maize plantation using a crop surface model. A simple algorithm was developed to fully analyse the maize plantation health and define the specific amount of requirements in the plots, which can be used in the application maps. The steps involved in generating the algorithm are written as follows:

Steps: ❖ Convert image into digital format/ array of numbers; ❖ Identify rows and columns with highest numbers; ❖ Calculate the difference between the higher numbers

and the lower numbers; ❖ Create a new table with the difference of the numbers; ❖ Equate the highest numbers to zero; ❖ Zero means no change; ❖ The numbers suggest the quantity of the input resource

be added; ❖ Check to be sure the additions are appropriate; ❖ Make a decision/end. The flow chart of the algorithm is represented in figure 8;

Figure 8: Flow chart used in formulating treatment plans from UAV images


J. Agric. Econ. Rural Devel. 289 The processes required in producing the colour map with its corresponding numbers in the colour map matrix are explained in the below paragraph. The raw images are set to the camera’s time and are calibrated with each pixel representing the reflectance value and not the colour value. Dark pictures are not a cause for concern during the analysis because of the image reflectance. An orthomosaic of the images is stitched together to contain all individual images taken. The images should be stitched together using location tags as identification before being used to create a map. The next step is to create patches of the image in Matlab, then crop the image into a square, an equal number of columns and rows (225 *225 matrix), and the image size can be created to suit its specific needs. This is subject to the image dimension required, for this image, the image is read from 1 to 224 making it a total of 225 pixels. Then, convert the image to an unsigned integer by using the ‘double’ function. This function scales the RGB image intensity from 0 – 1.The dimension of the image is 25 by 25 with 81 blocks of 9 by 9 rows and column, next, is slicing the image into grids, the image has 255 pixels, obtained by using the Matlab function to highlight the number of

pixels. The image is sliced with the slicing function created with codes created and imported into Matlab. Image slicing takes in two functions; the image and the number of slices. The function used is the ‘Nested for loop’ which performs iteration upon iteration, i.e. row by row then column by column. Note that while cropping, the images are stacked up. All of these processes is known as segmentation, and the result of the image is fed into the classifier to provide the specific treatment plan with the fuzzy rule-based classifier. Feature Extraction is the next function to be executed, using the segmented image, there are three (3) channels RGB (colour channel). All patches are not uniform. Hence the mean of the intensity of the image patch is computed. Each operation is repeated in each patch with the same instruction used above. In computing the intensity of the patches, the nitrogen translates to the chlorophyll content in the different patches on the field and the intensity is tracked for the most productive region in the field, which will be used as a reference to other field patches. The image classification generated using a fuzzy classifier approach is represented in Figure 9 and 10.

Figure 9 Figure 10 Figure 9: Sliced and segmented image into its 81 grids Figure 10: Results of reverse slicing showing possible

showing plantation with colour maps treatment plans The resulting values for the intensities are all saved up in a cell array in the string function, with each intensity being computed, the result is compared with the maximum intensity.

The reverse slicing is performed to produce the treatment plan required, which gives the exact quantity of nutrient required to make the specific patch as productive as the most productive patch on the field. The results are displayed to include the text superimposed on the image.

Areas labelled with 0 values are the most productive areas requiring no addition since they are very fertile while the

other areas highlight the required treatment quantity to make other grids as fertile as the most fertile region of the field.

With these classification results, grey areas are easily seen and treatment plans can be easily formulated for farm management decisions in the shortest time possible alongside other factors to be considered to accurately predict results for the solution required in the plantation such as the region, the weather, the soil type, stages of pests and disease infection, soil nutrient availability, soil water requirements, etc. for maximum production across the entire field grid.


Abdullahi and Sheriff 290 Recommendation for the Agricultural sector in Nigeria. As previously discussed, the nitrogen concentration in the green vegetation is linked to chlorophyll content and translates indirectly to the simplest plant physiological practises of photosynthesis (Moran et al., 1997), and then, the nitrogen loss to farmers represent an economic loss for farmland owners. The potential benefits of PA are savings in cost, more proficient use of production inputs, improved and better use of information technology to increase the size and scope of the farming operations without increasing labour requirements, enhanced site selection with control of production processes that aid in the production of higher value or specialty harvests, improved record keeping and production tracking for ensuring food safety and environmentally safe benefits (Steer, 2008). The most common presumption about the importance of precision agriculture is that it allows farmers to achieve a uniform and maximum production in the farm, but optimising input resources for the land with accordance to the capability of the land (McLoud P, 2007). PA, helps to make decisions on resource allocation to match soil and crop requirements better within their field variability. Precision technology is centred on information technology. Therefore farmers with lots of experience may argue that PA is not relevant with fewer years of experience but maybe better for young farmers who are less experienced as they are likely to accept and move towards innovations (McBride and Daberkow, 2003). To invest and grow the agricultural sector, agriculture needs to be taken as a long-term certainty with a government that behaves like a business. The government has a lot of roles to play regarding policy, the market, storage facilities. From the SWOT and PESTLE analysis, the opportunities and strength of growing the sector are numerous and without any doubt can be one of the largest leading producers of crops and fruits. This will increase food security, provide more job opportunities, contribute to international development while securing the environment. At the moment, agriculture is becoming one of the world’s fastest growing sectors with a breakthrough in nutrition, genetics, informatics, remote sensing, precision farming, etc. With the advantage of the Nigerian population estimated to be over 170 million (2013 census) making it the most populous country in Africa with a large market advantage. Increased production will feed more than half of the population while reducing the import of food resources at a reduced cost, which can be achieved by mechanisation and precision farming. There is Soft PA (involving soil testing) or hard PA (involves preventive measures from over application) both of which can be practised on any level to farmland. In Nigeria, land fragmentation challenges make

mechanisation difficult. Also, fertiliser consumption is low and wrongly applied due to the high variation in the field. Dynamic cheap soil testing will be suitable to the region before fertiliser application or planting. Areas of excessive application lead to stress to the area leading to runoff and water pollution. Research, data collection, and processing is required, development of a data bank and interconnection of data to available farm users and development of DSS computer model for making decisions on the farms. At present, there is a representative with the International Society of Precision Agriculture (ISPA), a non-profit professional scientific organisation with the mission to improve the science of PA worldwide. Nigeria has a representative that ensures that the following set of objectives are achieved: - Organising and conducting conferences related to PA

such as the International Conference on Precision Agriculture, the European Conference on Precision Agriculture etc.

- Developing and maintaining a web-portal to communicate the latest developments in PA with the world and maintaining a member listserv to communicate among the society members.

- Gives members the opportunity to publish original scientific research in the Society sponsored by peer-reviewed journals on PA.

The Nigerian centre located in Ibadan, Oyo state, is attached to Cocoa Research Institute of Nigeria (ISPA, 2015). With the necessary collaboration of ISPA with farm holder’s representatives, precision Agricultural technologies can be implemented in farms to achieve its benefits as highlighted earlier. CONCLUSION With most Nigerian farms being mostly disjointed small-scale farms, it is argued that precision agriculture will cost more than the advantages the technology proposes to offer but previous reports indicate that in some parts of the world, with precision agriculture, the advantages by far surpasses the losses encountered in the field to post-harvest losses, pest and disease attack and non-uniform plantation and also the reduction in cost of input resources. Nigerian agriculture depends 90% on fertiliser for nutrients applied in equal proportion to every portion of the field. The sample images clearly show areas of variation with the growth rate in the maize plantation. The treatment plan formulated can be used to apply herbicides, pesticides, fertilisers, water at the right amount, at the right time, right space giving a great chance for maximum production on the same field while preserving input resources, minimising cost and maximising profit. Information about how farmers should use PF to make managerial decisions about the relative extent of benefits and costs associated with the technologies. Also, the data can be used on the GPS guided system for application of input resources on


J. Agric. Econ. Rural Devel. 291

the farm with the treatment plan generated. Agricultural research institutes need to work closely with farmers and policymakers (Government) to determine the challenges faced by the farmers to develop technology initiative to help make the best use of these technologies. Having a larger data set will make simple image analysis overwhelming, which will require a more robust system, future work will be to develop and train a predictive computer model for simulation of standard input and output applications of farm input resources. The use of Convolution neural network is proposed to be used in the next research for classifying and identifying plant health conditions. Funding Sources The research conducted did not receive any grant from funding agencies in the public, profitable, or not-for-profit sectors. Declaration of Interest The authors declare that there is no conflict of interest with regards to publishing this paper. REFERENCES Abdullahi, H. S., Mahieddine, F. and Sheriff, R. E. (2015)

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Accepted 25 October 2017 Citation: Abdullahi HS, Sheriff RE (2017). Case Study to Investigate the Adoption of Precision Agriculture in Nigeria Using Simple Analysis to Determine Variability on a Maize Plantation. Journal of Agricultural Economics and Rural Development, 3(3): 279-292.

Copyright: © 2017 Abdullahi and Sheriff. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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