DataSheet_1_CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.pdf

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.

植物病害对作物产量与品质造成显著负面影响，对全球农业构成严重威胁。传统的植物病害识别与分类流程往往耗时耗力且易产生人为误差。本研究针对该问题，采用卷积神经网络（Convolutional Neural Network, CNN）与支持向量机（Support Vector Machine, SVM）混合模型，对草莓、桃、樱桃、大豆这四类经济作物的病害进行分类。本研究依托深度学习的CNN-SVM混合模型，旨在对上述四类作物的10类病害（含6类染病类别与4类健康类别）完成分类。本研究同时训练了多款预训练模型，包括VGG16、VGG19、DenseNet、Inception、MobileNetV2、MobileNet、Xception及ShuffleNet，其分类准确率介于53.82%至98.8%之间。而本研究提出的CNN-SVM混合模型，平均分类准确率可达99.09%。尽管该模型的准确率与预训练VGG16模型相当，但其可训练参数数量显著更少，因此具备更高的运行效率与独特优势。本研究验证了CNN-SVM混合模型在提升植物病害分类准确率与效率方面的应用潜力。相较于VGG16及其他模型，本研究选择CNN-SVM混合模型的原因在于其更优异的性能指标。该模型实现了99%的F1分数、99.98%的曲线下面积（Area Under the Curve, AUC）以及99%的精确率，充分证明了其分类效能。此外，本研究采用梯度加权类激活映射（Gradient Weighted Class Activation Mapping, Grad-CAM）技术生成类激活图，对检测到的病害提供可视化解释。同时生成了热图以高亮显示待分类的关键区域，进一步验证了模型的分类准确性与可解释性。