Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise.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

翻译：与拥有更多资源和专业知识的大型种植园不同，小规模可可种植者通常依赖过时的耕作技术，并面临来自病虫害的重大挑战。在菲律宾，可可种植户获取数据、信息和良好农业实践的机会有限。本研究通过开发一款用于可可病害识别与管理的移动应用程序来解决这些问题，该应用支持离线运行，可在农场主要所在的偏远地区使用。系统的核心是一个经过训练以准确识别可可病害的深度学习模型。训练好的模型被集成到移动应用中，以支持种植户进行田间诊断。病害识别模型的验证准确率达到96.93%，而用于检测可可黑果病感染程度的模型则达到了79.49%的验证准确率。该应用的实地测试显示，与可可专家技术员的评估相比，其一致率达到84.2%。这种方法通过提供易于获取、技术赋能的工具来改善可可作物的健康和生产力，从而增强了小规模种植户的能力。