Dataset utilized.

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.

玉米（Zea mays）作物的叶部病害对玉米产量的品质与体量均会造成显著负面影响，最终波及市场行情。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区扩散蔓延。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），用于玉米作物叶部病害的检测、多分类以及严重程度评估。本研究所选取的7类玉米作物病害与健康状态样本，均取自公开数据集PlantVillage，具体包括：北方叶枯病（Northern Leaf Blight，病原菌为Exserohilum turcicum）、眼斑病（Eye Spot，病原菌为Oculimacula yallundae）、普通锈病（Common Rust，病原菌为Puccinia sorghi）、戈斯氏细菌性萎蔫病（Goss’s Bacterial Wilt，病原菌为Clavibacter michiganensis subsp. nebraskensis）、霜霉病（Downy Mildew，病原菌为Pseudoperonospora）、小球腔菌叶斑病（Phaeosphaeria leaf spot，病原菌为Phaeosphaeria maydis）、灰斑病（Gray Leaf Spot，病原菌为Cercospora zeae-maydis），以及健康样本。在识别病害侵染区域后，本模型采用EfficientNet-B7完成特征提取。为实现玉米病害侵染情况的分类，本研究采用混合哈里斯鹰优化算法（HHHO）完成特征选择。最后，基于所得到的优化特征，借助模糊支持向量机（Fuzzy SVM）完成分类与病害严重程度评估。本研究开展了实验分析，以验证所提方法在玉米叶部病害检测与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%。本研究成果有助于推动农业领域自动化病害诊断技术的发展，进而为作物产量可持续提升与粮食安全保障工作提供支撑。