Cacao Plant Disease Detection and Classification

Abstract Food security is a vital aspect of the United Nations’ Sustainable Development Goals (SDGs) which aims to promote sustainable farming in the world. Farming-driven economies such as Ghana are faced with challenges due to plant diseases. Cacao, a vital crop in Ghana is severely impacted with diseases which affect its yield and decrease exports revenue through reduced exports. Leveraging deep learning techniques offers an effective solution for early detection of diseases in cacao plants. This study adopts a comprehensive approach, starting with an Exploratory Data Analysis (EDA) of the dataset containing images of both healthy and diseased cacao plants from Ghanaian farms. Using exploratory data analysis (EDA), we can identify patterns and understand the characteristics of the dataset, laying a solid foundation for developing robust machine learning models tailored to the specific challenges faced by Ghanaian cacao farmers. Our approach involves developing and evaluating deep learning models to detect and classify cacao plant diseases. These models are designed with the Predictability, Compatibility, and Stability (PCS) framework in mind, ensuring reliability and effectiveness in disease detection. The custom convolution neural network (CNN) model outperformed other models considered in experimental analysis. This study aims to revolutionize cacao farming through precise, stable, and ethical deep learning solutions, ultimately enhancing crop resilience, productivity, and the livelihood of Ghanaian farmers.