Development of a Cacao Disease Identification and Management App Using Deep Learning

TL;DR

This paper presents a mobile app utilizing deep learning to accurately identify cacao diseases offline, aiding smallholder farmers in remote areas to improve crop health and productivity.

Contribution

It introduces a novel offline-capable mobile application with a deep learning model for cacao disease diagnosis tailored for resource-limited farmers.

Findings

Disease identification accuracy of 96.93%

Detection of black pod infection with 79.49% accuracy

Field testing showed 84.2% agreement with expert assessments

Abstract

Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise.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.