Designing a Machine Learning Model for Crop Pest Surveillance among Small Holder Farmers in Uasin Gishu County, Kenya

Abstract

Uasin Gishu County is noted as a breadbasket region in Kenya. This county provides a big proportion of the rural smallholder farmers’ income because 90% of its land is arable. It is endowed with high and consistent rainfall and favorable cropping temperatures. Maize, wheat, beans, and Irish potatoes are the common food crops in the county. However, crop productivity in the region is currently tapering off due to the use of traditional mechanisms to mitigate and control emerging crop pests and their effects. This coupled with inadequate extension services and hardly accessible information from agricultural agencies both at the county and the national governments has greatly contributed to a decline in agricultural production in the county. Crop pest surveillance by small holder farmers has been sub optimal while using ICT tools such as mobile phone. Notably, this challenge can be mitigated by leveraging mobile technology in building digital solutions that can provide farmers with easily accessible, precise, and timely information. Other technologies used in modern world applications include Machine Learning (ML), a branch of artificial intelligence. The digital solution in this case is ML technology which has proved to minimize losses incurred in farming. In solving the real-world problems, ML has found its use in predicting commodity prices, detecting fraudulent transactions, treatment and diagnosis of diseases, prediction of energy use and image recognition among other uses. It has increased efficiency and precision in farming thereby guaranteeing high quality farm output. The study aimed at exploring potential of ICT tools in providing information access to farmers by leveraging mobile technology. The main objectives looked at establishing usage of mobile phones for crop pest surveillance in Kenya, to design a machine learning model for crop pest surveillance for small holder farmers, and to evaluate the proposed machine learning model for crop pest surveillance among small holder farmers using mobile phones. The study targeted farmers in Kesses Sub County involved in small scale crop farming. Stratified sampling technique was used to select 3 Wards in Kesses Sub County that have had the highest hit of emerging crop pests. The targeted sampling technique was used to select farmers who experienced greatest loss due to crop pest between 2017 and 2021. The study involved mixed methods research design, and system analysis design methodologies. In mixed methods design, questionnaires were administered to respondents. Statistical methods employed was descriptive statistics and correlation analysis. In analyzing qualitative data, content analysis technique was applied. In systems analysis and design, this described how the development of the system will be achieved using the four-phased model which are planning, analysis, design, and implementation. Insights from field data showed that most farmers, 45 percent are using mobile phones in sourcing agriculture information. This was used to inform design of machine learning model for crop pest surveillance. This was achieved by use of plant disease detection, processing, segmentation, extraction, and classifier algorithms. In supporting the testing of the algorithm, accuracy, precision, recall, and F1 measurement score was used because this is highly supported in literature results with over 90% scores, which is within the acceptable score for testing. This study supports the use of mobile phone as one of key tools in carrying out crop pest surveillance by small holder farmers.