Sample of dataset.

Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detection of the intensity of an infection would enable the efficient allocation of treatment resources and prevent the infection from spreading across the entire area. In this study, deep saliency map segmentation-based CNN is utilized for the detection, multi-class classification, and severity assessment of maize crop leaf diseases has been proposed. The proposed model involves seven different maize crop diseases such as Northern Leaf Blight Exserohilum turcicum, Eye Spot Oculimacula yallundae, Common Rust Puccinia sorghi, Goss’s Bacterial Wilt Clavibacter michiganensis subsp. nebraskensis, Downy Mildew Pseudoperonospora, Phaeosphaeria leaf spot Phaeosphaeria maydis, Gray Leaf Spot Cercospora zeae-maydis, and Healthy are selected from publicly available datasets obtained from PlantVillage. After the disease-affected regions are identified, the features are extracted by using the EffiecientNet-B7. To classify the maize infection, a hybrid harris hawks’ optimization (HHHO) is utilized for feature selection. Finally, from the optimized features obtained, classification and severity assessment are carried out with the help of Fuzzy SVM. Experimental analysis has been carried out to demonstrate the effectiveness of the proposed approach in detecting maize crop leaf diseases and assessing their severity. The proposed strategy was able to obtain an accuracy rate of around 99.47% on average. The work contributes to advancing automated disease diagnosis in agriculture, thereby supporting efforts for sustainable crop yield improvement and food security.玉蜀黍（Zea mays）作物的叶片病害会显著影响玉米产量的品质与规模，最终对市场造成冲击。提前检测病害侵染程度，可实现防治资源的高效配置，并阻止病害在整片田块扩散。 本研究提出了一种基于深度显著性图分割的卷积神经网络（Convolutional Neural Network, CNN），用于玉米作物叶片病害的检测、多分类及严重程度评估。 本研究从公开数据集PlantVillage中选取了7种不同的玉米作物病害及健康样本，具体包括：北方叶枯病（病原为突脐蠕孢菌Exserohilum turcicum）、眼斑病（Oculimacula yallundae）、普通锈病（Puccinia sorghi）、戈斯细菌性枯萎病（Clavibacter michiganensis subsp. nebraskensis）、霜霉病（Pseudoperonospora属）、小球腔菌叶斑病（Phaeosphaeria maydis）以及灰斑病（Cercospora zeae-maydis），同时纳入健康叶片样本。 在识别病害侵染区域后，本研究采用高效网络B7（EfficientNet-B7）提取特征。 为实现玉米病害侵染的分类，本研究采用混合哈里斯鹰优化算法（Harris Hawks Optimization, HHHO）进行特征选择。 最后，基于提取得到的优化特征，本研究借助模糊支持向量机（Fuzzy Support Vector Machine, Fuzzy SVM）完成病害分类与严重程度评估。 本研究开展了实验分析，以验证所提方法在玉米作物叶片病害检测及严重程度评估中的有效性。 所提策略的平均准确率可达约99.47%。 本研究有助于推动农业领域病害自动诊断技术的发展，进而为可持续提升作物产量及保障粮食安全提供支撑。