基于深度学习农作物病虫害检测识别的研究进展
西南林业大学, 机械与交通学院, 云南昆明 650224
| 摘 要： | 农作物在全球食品供应中发挥着至关重要的作用。各种环境因素都会导致农作物病虫害，从而导致严重的生产损失。然而，人工检测作物病虫害是一个耗时且容易出错的过程，往往无法及时有效的识别和预防植物病害的传播。为了克服这些挑战，近年来引入了机器学习（ML）和深度学习（DL）技术，以提高对植物病虫害的早期识别和有效应对能力。在本文中，阐述了卷积神经网络（CNN）、循环神经网络（RNN）、对抗式生成神经网络（GAN）原理，并讨论了三种算法在检测植物病虫害中的改进及应用的最新进展。通过应用DL技术，可以实现对病虫害的高效、准确检测，从而为农业生产提供可靠的支持。希望这项工作能够成为研究植物病虫害检测的研究人员的宝贵资源，为进一步推动该领域的研究和实践提供指导。同时，还讨论了当前面临的一些挑战和需要解决的问题，例如数据质量、目标遮挡、模型泛化能力以及实际应用中的适应性等方面。深度学习通过为病虫害管理提供高效、准确的解决方案，在精准农业革命方面具有巨大的前景。 |
| 关 键 词： | 深度学习; 农作物病虫; 研究进展 |
| DOI： | 10.57237/j.cst.2024.02.002 |
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收稿日期: 2024-02-26
接受日期: 2024-04-19
在线出版日期: 2024-04-24
Research Progress in Detection and Identification of Crop Diseases and Insect Pests Based on Deep Learning
College of Mechanical and Transportation, Southwest Forestry University, Kunming 650224, China
| Abstract: | Crops play a vital role in the global food supply. Various environmental factors can cause crop diseases and insect pests, resulting in severe production losses. However, manual detection of crop diseases and insect pests is a time-consuming and error-prone process, which often fails to identify and prevent the spread of plant diseases in a timely and effective manner. To overcome these challenges, machine learning (ML) and deep learning (DL) technologies have been introduced in recent years to improve early identification and effective response to plant pests and diseases. In this article, the principles of convolutional neural network (CNN), recurrent neural network (RNN) and adversarial generative neural network (GAN) are explained, as well as the latest progress in the improvement and application of the three algorithms in plant disease detection and detection. By applying DL technology, efficient and accurate detection of pests and diseases can be achieved, thereby providing reliable support for agricultural production. It is hoped that this work will become a valuable resource for researchers studying plant pest and disease detection and provide guidance to further advance research and practice in this field. At the same time, some current challenges and issues that need to be solved are also discussed, such as data quality, target occlusion, model generalization ability, and adaptability in practical applications. Deep learning holds great promise in revolutionizing precision agriculture by providing efficient and accurate solutions for pest and disease management. |
| Keywords: | Deep Learning; Crop Diseases and Pests; Research Progress |
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