Hadipour-Rokni Ramazan, Askari Asli-Ardeh Ezzatollah, Jahanbakhshi Ahmad, Esmaili Paeen-Afrakoti Iman, Sabzi Sajad
Department of Biosystem Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
Comput Biol Med. 2023 Mar;155:106611. doi: 10.1016/j.compbiomed.2023.106611. Epub 2023 Feb 1.
Plant pests and diseases play a significant role in reducing the quality of agricultural products. As one of the most important plant pathogens, pests like Mediterranean fruit fly cause significant damage to crops and thus annually farmers face a lot of loss in their products. Therefore, the use of modern and non-destructive methods such as machine vision systems and deep learning for early detection of pests in agricultural products is of particular importance. In this study, citrus fruit images were taken in three stages: 1) before pest infestation, 2) beginning of fruit infestation, and 3) eight days after the second stage, in natural light conditions (7000-11,000 lux). A total of 1519 images were prepared for all classes. To classify the images, 70% of the images were used for the network training stage, 10% and 20% of the images were used for the validation and testing stages. Four pre-trained CNN models, namely ResNet-50, GoogleNet, VGG-16 and AlexNet as well as the SGDm, RMSProp and Adam optimization algorithms were used to identify and classify healthy fruit and fruit infected with the Mediterranean fly. The results of evaluating the models in the pest outbreak stage showed that the VGG-16 model with the help of SGDm algorithm had the best efficiency with the highest detection accuracy and F1 of 98.33% and 98.36%, respectively. The evaluation of the third stage showed that the AlexNet model with the help of SGDm algorithm had the best result with the highest detection accuracy and F1 of 99.33% and 99.34%, respectively. AlexNet model using SGDm optimization algorithm had the shortest network training time (323 s). The results of this study showed that convolutional neural network method and machine vision system can be effective in controlling and managing pests in orchards and other agricultural products.
植物病虫害在降低农产品质量方面起着重要作用。作为最重要的植物病原体之一，诸如地中海实蝇之类的害虫会对农作物造成重大损害，因此农民每年在农产品上都会面临巨大损失。因此，使用机器视觉系统和深度学习等现代无损方法来早期检测农产品中的害虫尤为重要。在本研究中，柑橘类水果图像分三个阶段拍摄：1）害虫侵染前；2）水果开始被侵染；3）第二阶段八天后，在自然光条件下（7000 - 11000勒克斯）。总共为所有类别准备了1519张图像。为了对图像进行分类，70%的图像用于网络训练阶段，10%和20%的图像分别用于验证和测试阶段。使用了四个预训练的卷积神经网络（CNN）模型，即ResNet - 50、GoogleNet、VGG - 16和AlexNet，以及随机梯度下降动量（SGDm）、均方根传播（RMSProp）和自适应矩估计（Adam）优化算法来识别和分类健康水果以及感染地中海实蝇的水果。在害虫爆发阶段对模型进行评估的结果表明，借助SGDm算法的VGG - 16模型效率最高，检测准确率和F1值分别为98.33%和98.36%。第三阶段的评估表明，借助SGDm算法的AlexNet模型效果最佳，检测准确率和F1值分别为99.33%和99.34%。使用SGDm优化算法的AlexNet模型网络训练时间最短（323秒）。本研究结果表明，卷积神经网络方法和机器视觉系统可有效用于果园及其他农产品的害虫防治和管理。
