Abstract:
In Kenya, coffee farming faced various challenges, including widespread pests and diseases that endangered both the quality and yield of coffee. Traditional farming mechanisms were unable to provide timely interventions, which led to economic losses for farmers. The main objective of this study was to develop a hybrid machine learning model for the accurate prediction of coffee diseases in Kenya. The developed hybrid model combined Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance the accuracy of disease prediction in coffee crops. CNN extracted spatial features from leaf images, while LSTM captured temporal patterns from environmental and agronomic data. This approach enabled early and precise detection of diseases in Kenyan coffee farms. By using this solution, coffee farmers were able to improve disease management, thereby optimizing coffee yields. The use of this technique was aimed at facilitating the early detection of major potential threats to coffee production in Kenya. The study further outlined the methodologies, outcomes, and long-term implications for local farming and the wider coffee industry in Kenya.
