Department of Computer Science, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.
Department of Mathematics, Chabahar Maritime University, Chabahar, Iran.
J Sci Food Agric. 2022 Dec;102(15):6907-6920. doi: 10.1002/jsfa.12052. Epub 2022 Jun 25.
Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore, the damage to the tomato tree must be controlled in its early stages. It is difficult for farmers to distinguish Alternaria disease from leafminer pest at the early and middle stages of their outbreak on tomato leaves. In the present study, 272 tomato leaf images were prepared from the farm of the Vali-e-Asr University of Rafsanjan, including 100 healthy leaves and 172 infected leaves with both Alternaria and leafminer at the initial stages. The image processing technique, texture, neural networks and adaptive network-based fuzzy inference system (ANFIS) classifiers were used to diagnose Alternaria disease and leafminer pest on this dataset.
The results showed that the ANFIS classifier achieved an accuracy of 84.71% when performing an equal error rate, 87.78% in the area under the curve, and 85.23% in 3.26 s on the central processing unit for the segmentation of Alternaria disease and leafminer pest in RGB color space. Also, the accuracy of 90% and 98% were obtained for segmentation and classification on the PlantVillage dataset in YCBCR color space.
The present study suggests a high classification accuracy for an intelligent selection of pixel values to train the ANFIS classifier. This classifier has high accuracy and speed, low sensitivity to the light intensity of images, and practical application in diagnosing various diseases and pests on numerous datasets. © 2022 Society of Chemical Industry.
番茄作为用途最广泛的农产品之一，其叶片易受链格孢菌等病害和潜叶蝇等虫害的威胁。这些病虫害先从番茄叶片开始侵害，然后是花朵，最后是果实。因此，必须在早期控制对番茄树的损害。农民很难在链格孢菌病和潜叶蝇虫害爆发的早期和中期区分开来。在本研究中，从拉夫桑詹的瓦尔-埃-阿斯尔大学的农场准备了 272 张番茄叶片图像，包括 100 张健康叶片和 172 张在初期同时感染链格孢菌和潜叶蝇的叶片。本研究使用图像处理技术、纹理、神经网络和自适应网络模糊推理系统（ANFIS）分类器对该数据集进行了链格孢菌病和潜叶蝇虫害的诊断。
结果表明，当在 RGB 颜色空间进行等错误率分割时，ANFIS 分类器的准确率为 84.71%，在 ROC 曲线下面积为 87.78%，在中央处理器上用时为 3.26 秒；在 YCBCR 颜色空间的 PlantVillage 数据集上，分割和分类的准确率分别为 90%和 98%。
本研究表明，智能选择像素值可以训练出具有高分类精度的 ANFIS 分类器。该分类器具有准确率和速度高、对图像光照强度不敏感、在多个数据集上诊断各种病虫害的实际应用等优点。© 2022 化学工业学会。
