用深度学习打造离线农技助手，帮小农户精准识别可可病害
Development of a Cacao Disease Identification and Management App Using Deep Learning
- 训练深度学习模型，离线部署于手机应用实现病害识别
- 病害识别准确率达96.93%，黑斑病感染程度检测达79.49%
- 实地测试与专家判断一致率达84.2%，适合资源匮乏地区农民使用
小农户常依赖过时的种植技术，面临虫害和病害严重威胁，而菲律宾的可可种植者普遍缺乏数据、信息与良好农业实践支持。本研究开发了一款可在离线环境下运行的移动应用，用于可可病害识别与管理，适用于多数偏远地区的农场。系统核心为深度学习模型，经训练可准确识别可可病害。该模型在验证集上达到96.93%的准确率，黑斑病感染程度检测模型验证准确率为79.49%。应用实地测试显示，与专业可可技术员评估结果的一致率达84.2%。该方案为小农户提供了可访问的技术工具，有助于提升可可作物健康与产量。
原文摘要 · Abstract (English)
Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise.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.
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