Abstract
Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these diseases can significantly mitigate crop loss. In this study, we propose a deep learning-based approach for efficient detection of plant diseases using drone-captured imagery. A comprehensive database of various plant species, exhibiting numerous diseases, was compiled from the Internet and utilized as the training and test dataset. A Convolutional Neural Network (CNN), renowned for its performance in image classification tasks, was employed as our primary predictive model. The CNN model, trained on this rich dataset, demonstrated superior proficiency in crop disease categorization and detection, even under challenging imaging conditions. For field implementation, we deployed a prototype drone model equipped with a high-resolution camera for live monitoring of extensive agricultural fields. The captured images served as the input for our trained model, enabling real-time identification of healthy and diseased plants. Our approach promises an efficient and scalable solution for improving crop health monitoring systems.
Abstract (translated)
监测植物健康对于维持农业生产力和食品安全至关重要。由疾病引起的植物正常状态的 disruption 常常干扰 essential 植物活动，而及时检测这些疾病可以显著减轻作物损失。在本研究中，我们提出了一种基于深度学习的方法，利用无人机捕获的图像来进行高效的植物疾病检测。从互联网上收集了多种植物物种的全面数据库，展示了许多疾病，作为训练和测试数据集使用。我们采用了著名的卷积神经网络(CNN)，因其在图像分类任务中的表现而知名，将其作为主要预测模型。通过训练这个丰富的数据集，CNN 模型在作物疾病分类和检测方面表现出卓越的能力，即使在挑战性的图像处理条件下也是如此。为了在实践中实现，我们部署了一台配备了高分辨率相机的原型无人机，用于实时监测广泛的农田。捕获的图像作为我们训练模型的输入，实现了实时识别健康和患病的植物。我们的方法提供了一个高效且可扩展的解决方案，以改善作物健康监测系统。
URL
https://arxiv.org/abs/2305.13490
