A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection

TL;DR

This paper introduces a low-cost, UAV-based system using RGB cameras and deep learning models for comprehensive apple orchard management, including disease detection, fruit freshness assessment, and fruit localization, all operating offline on affordable hardware.

Contribution

It presents a unified, low-cost UAV pipeline integrating multiple deep learning models for orchard tasks, eliminating the need for expensive multispectral sensors and cloud services.

Findings

98.9% accuracy in leaf disease classification

97.4% accuracy in apple freshness detection

0.857 F1 score for apple detection

Abstract

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.