Mobile Application with Convolutional Neural Networks for the Early Detection of Diseases in Blueberry Plants in Chepén: Trujillo

Orejuela S.S.H.; Garay A.M.C. · Proceedings of the International Conference on Informatics in Control, Automation and Robotics (2025) · DOI: 10.5220/0013676800003982

Resumen

Early detection of foliar diseases in blueberry crops is essential to protect yield and fruit quality, especially in Chepén–Trujillo, a key agricultural region in Peru. This paper presents a mobile application developed with Flutter and powered by a lightweight convolutional neural network (CNN), capable of analyzing leaf images and delivering disease diagnoses in under three seconds. The system supports offline functionality, ensuring usability in rural areas with limited connectivity. In a test set of 350 images, the model achieved 93% accuracy, 88% recall, and an F1 score of 0.90. Field validation with local farmers showed 90% agreement with expert diagnoses. Beyond its technical performance, this solution has the potential to reduce economic losses, improve crop quality, and empower smallholder farmers through accessible, real-time diagnostics. The platform is scalable to other crops and regions, contributing to more sustainable and resilient agricultural practices in Peru.

Instituciones: Universidad Peruana de Ciencias Aplicadas