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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.00216 |
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| _version_ | 1866908800963837952 |
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| author | Pagaduan, Zaldy Occidental, Jason Duro, Nathaniel Badilles, Dexielito Palconit, Eleonor |
| author_facet | Pagaduan, Zaldy Occidental, Jason Duro, Nathaniel Badilles, Dexielito Palconit, Eleonor |
| contents | Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00216 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Development of a Cacao Disease Identification and Management App Using Deep Learning Pagaduan, Zaldy Occidental, Jason Duro, Nathaniel Badilles, Dexielito Palconit, Eleonor Computer Vision and Pattern Recognition Computers and Society Image and Video Processing I.4; J.3 Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise.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. |
| title | Development of a Cacao Disease Identification and Management App Using Deep Learning |
| topic | Computer Vision and Pattern Recognition Computers and Society Image and Video Processing I.4; J.3 |
| url | https://arxiv.org/abs/2602.00216 |