A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection
This provides an accessible and scalable solution for precision agriculture, addressing multiple tasks in isolation with affordable hardware, though it is incremental as it combines existing methods.
The paper tackled the problem of integrated disease diagnosis, freshness assessment, and fruit detection in apple orchards using a low-cost UAV system, achieving 98.9% accuracy for leaf disease classification, 97.4% for freshness classification, and a 0.857 F1 score for apple detection.
Apple orchards require timely disease detection, fruit quality assessment, and yield estimation, yet existing UAV-based systems address such tasks in isolation and often rely on costly multispectral sensors. This paper presents a unified, low-cost RGB-only UAV-based orchard intelligent pipeline integrating ResNet50 for leaf disease detection, VGG 16 for apple freshness determination, and YOLOv8 for real-time apple detection and localization. The system runs on an ESP32-CAM and Raspberry Pi, providing fully offline on-site inference without cloud support. Experiments demonstrate 98.9% accuracy for leaf disease classification, 97.4% accuracy for freshness classification, and 0.857 F1 score for apple detection. The framework provides an accessible and scalable alternative to multispectral UAV solutions, supporting practical precision agriculture on affordable hardware.