Machine Learning-Based Crop Monitoring System in Kenya

1. Creating smoother pathways to scale up research diffusion and specific innovations
2. Mobilizing a collective research and innovation ecosystem approach to address a particular development challenge
3. Building informed human capital through the enhancement of research and innovation skills
4. Building a framework for assurance and enhancement of research quality, uptake and impact
5. Establishing supportive research and innovation infrastructure

Farmers in Kenya, as in many parts of the world, experience perennial financial losses due to poor yields caused by crop diseases, pests and nutritional deficiencies. This is largely due to the unavailability of effective crop monitoring systems that can detect early stages of diseases and pests’ invasion. Late detection of pests, diseases and nutrition deficiencies leads to spreading of the disease's infection to the entire farm causing irreversible damages and expensive operating costs, especially in crops such as rice, tomatoes and coffee. To avoid the spread of the infections farmers resolve to use of expensive preventive measures which involve application of farm chemicals and fertilizers to the entire plantations.

The proposed solution will aim at developing a system that will enable early detection of diseases, pests and nutritional deficiency hence allowing early control practices which target only the affected sections. The Farmers will be able to identify portions of the farm affected and target them in the application of the appropriate control measures leading to less wastage of farm materials and less operating costs hence better crop yields.

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Agriculture in the Kenyan economy, it suffers perennial food insecurity, high rates of unemployment, high Agricultural operation costs and poverty. The project will offer the solution to these problems by allowing early detection of disease, pest or deficiencies hence early and targeted treatments which focuses only on the few affected crops. Upon commercialization, it will lead to better overall yield leading to better food security for the community, more sales, more economic activities, more employment along the value chain, more exports and better economic growth and less poverty among the citizens. Other stakeholders including; agricultural value chain operators, the retail traders, the agricultural manufacturing organizations, and the agricultural produce exporters will benefit.

It will provide opportunities for economic growth by contributing to the national GDP (wealth creation) through the utilization of otherwise dormant resources while promoting socio-social inclusivity, economic empowerment, poverty eradication, income generation, and improved living standards.

• KCA University will provide an innovation platform for the researchers during the innovation implementation process. This will help in further development of the innovation ecosystem in the university as the innovations developed in the institution are diffused to the local community.
• There will be collaboration between rice growers and community groups who will be the recipients of the innovation and this will lead to technology transfer from the institution of learning to points where the innovation can help in value creation.
• The researchers will also collaborate with the county governments in targeted rice growing areas which will have a positive impact in rice sector revival and technology transfer within the counties. 
• There will also be collaboration between the research team and the rice milling factories who will offer solutions on early detection of crop diseases through the innovation being implemented. This will improve the rice production process and hence create avenues for more research in the industry.
• The researchers will also collaborate system developers in developing the machine learning which will thus enhance their skills in local innovation development. 

The project will provide opportunities for learning and further research by the postgraduate students of institutions of higher learning and researchers from other collaborating, learning, and research institutions in Kenya and in the region. The farmers will be sensitized and educated on the eco-friendly agricultural systems of timely disease detection with will promote sustainable environmental management systems for sustainable agricultural productivity. The proposed research will aim at identification, procurement and installation of multispectral cameras which will be able to detect color changes, growth deficiencies and pests in the crop. The cameras will be mounted on high poles, high grounds and remote-controlled drones which will provide an aerial view of the targeted plantation or farm. The research team will then develop a computerized system that will receive data from the cameras and will be programmed to interpret the data in relation to the type of disease, pests and deficiencies.

-       Agriculture is the backbone of the Kenyan economy. Agricultural products account for 65% of the Kenyan exports and provides livelihood to over 80% of the population. Yet Kenya suffers perennial food insecurity, high rates of unemployment, high Agricultural operation costs and poverty among other challenges which can be overcome through innovative farm management systems. These systems such as the proposed one will offer the solution to these problems by allowing early detection of disease, pest or deficiencies hence early and targeted treatments which focuses only on the few affected crops. This will mean that the diseases, deficiencies and pest will not spread to other crops. The project will also lead to better Innovation in a localized manner.

it will strengthen the enablers for scaling of new and emerging technologies with high potential for in crop production hence poverty reduction, economic empowerment and inclusive growth focusing on the Agriculture and Manufacturing Sector

The solution will provide opportunities for learning and further research by the postgraduate students of institutions of higher learning and researchers from other collaborating, learning, and research institutions in Kenya and in the region. The farmers will be sensitized and educated on the eco-friendly agricultural systems of timely disease detection with will promote sustainable environmental management systems for sustainable agricultural productivity. The proposed research will aim at identification, procurement and installation of multispectral cameras which will be able to detect color changes, growth deficiencies and pests in the crop. The cameras will be mounted on high poles, high grounds and remote-controlled drones which will provide an aerial view of the targeted plantation or farm. The research team will then develop a computerized system that will receive data from the cameras and will be programmed to interpret the data in relation to the type of disease, pests and deficiencies.

the solution will provide opportunities for economic growth by contributing to the national GDP (wealth creation) through the utilization of otherwise dormant resources while promoting socio-social inclusivity, economic empowerment, poverty eradication, income generation, and improved living standards.

The proposed project seeks to empower the rural local farmers, women and agri-preneurs on disease detection strategies of the rice. Localizing high quality rice milling process and developing the rice value chain has the ability to create numerous employment opportunities. This, in turn, is expected to lead to improved household income, reduction of poverty, improved living standards and overall economic growth.

This theory of change adopts an inclusive business model that seeks to create value for low-income communities by integrating them into the rice value chain:

1. Supporting rice farmers in adding value to their rice and disease detection well in advance which will make it possible for the farmers increase their productivity. Training them on the innovation will encourage them to sell value added outputs.

2.         Integrating the farmers to the market: selling high quality produce will help achieve high economic independence among the poor rural population.

the solution will be engaged in the development, analysis, testing,
optimization, piloting and reporting on the proposed automated diseases, pests and nutritional deficiency monitoring system using apps, SMS technology, software, AI, robots,  and drones. The solution will be domiciled in the rural rice growing areas in Kenya hence will also adopt the use of the locally available materials in the development. It will be composed of highly qualified researchers and junior researchers in the fields of agriculture, computer science, business and biochemistry

Agriculture is the backbone of the Kenyan economy. Agricultural products account for 65% of the Kenyan exports and provides livelihood to over 80% of the population. Yet Kenya suffers perennial food insecurity, high rates of unemployment, high Agricultural operation costs and
poverty among other vices which can be overcome through innovative farm management systems. These systems such as the proposed one will offer the solution to these problems by
allowing early detection of disease, pest or deficiencies hence early and targeted treatments which focuses only on the few affected crops. This will mean that the diseases, deficiencies and pest will not spread to other crops.
Once the system is commercialized and applied in large scale, it will lead to better overall yield leading to better food security for the community, more sales, more economic activities, more employment along the value chain, more exports and better economic growth and less poverty among the citizens. The proposed research project will be beneficial to not only the farm owners but also to a host of other stakeholders including the agricultural value chain operators, the retail traders, the agricultural manufacturing organizations, and the agricultural produce exporters
among others.

The project will be carried out in a learning institution and therefore will provide opportunities for learning and further research by the postgraduate students of KCA University and researchers from other collaborating, learning, and research institutions. The farmers will be sensitized and educated on the eco-friendly agricultural crop diseases monitoring systems with will promote sustainable environmental management systems for sustainable agricultural productivity. 

The team proposes the use of a Multispectral camera because the human eye can only detect wavelengths between 400 and 700 mm. However, there are other wavelengths that the human eye cannot be able to detect and these wavelengths are critical to the early detection of crop diseases and nutrient deficiencies. The proposed agricultural innovation will make use of a Multispectral camera because these cameras have the ability to detect these wavelengths that are not visible to the human eye and have very important agronomic characteristics of plants and crops that will aid in the early detection of crop diseases

The proposed research will aim at developing a system that will enable early detection of
diseases, pests and nutritional deficiency hence allowing early control practices which target
only the affected sections. The Farmers will be able to identify portions of the farm affected and

target them in the application of the appropriate control measures leading to less wastage of farm
materials and less operating costs hence better crop yields.

Agriculture is the backbone of the Kenyan economy. Agricultural products account for 65% of
the Kenyan exports and provides livelihood to over 80% of the population. Yet Kenya suffers
perennial food insecurity, high rates of unemployment, high Agricultural operation costs and
poverty among other vices which can be overcome through innovative farm management
systems. These systems such as the proposed one will offer the solution to these problems by
allowing early detection of disease, pest or deficiencies hence early and targeted treatments
which focuses only on the few affected crops. This will mean that the diseases, deficiencies and
pest will not spread to other crops.
Once the system is commercialized and applied in large scale, it will lead to better overall yield
leading to better food security for the community, more sales, more economic activities, more
employment along the value chain, more exports and better economic growth and less poverty
among the citizens. The proposed research project will be beneficial to not only the farm owners
but also to a host of other stakeholders including the agricultural value chain operators, the retail
traders, the agricultural manufacturing organizations, and the agricultural produce exporters
among others