Development of a Cacao Disease Identification and Management App Using Deep Learning
It addresses limited access to agricultural information for smallholder cacao farmers in the Philippines by providing an offline tool, though it is incremental as it applies existing deep learning methods to a new domain.
The study developed a mobile app using deep learning to identify cacao diseases and manage infections, achieving validation accuracies of 96.93% for disease identification and 79.49% for black pod infection levels, with field testing showing 84.2% agreement with expert assessments.
Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise. In the Philippines, cacao farmers have limited access to data, information, and good agricultural practices. This study addresses these issues by developing a mobile application for cacao disease identification and management that functions offline, enabling use in remote areas where farms are mostly located. The core of the system is a deep learning model trained to identify cacao diseases accurately. The trained model is integrated into the mobile app to support farmers in field diagnosis. The disease identification model achieved a validation accuracy of 96.93% while the model for detecting cacao black pod infection levels achieved 79.49% validation accuracy. Field testing of the application showed an agreement rate of 84.2% compared with expert cacao technician assessments. This approach empowers smallholder farmers by providing accessible, technology-enabled tools to improve cacao crop health and productivity.