Chilakalapudi Malathi, Jayachandran Sheela
SCOPE, VIT-AP University, Amaravathi, Andhra Pradesh, India.
PeerJ Comput Sci. 2025 Jun 11;11:e2543. doi: 10.7717/peerj-cs.2543. eCollection 2025.
The major challenges that the agricultural sector faces are that with the kind of methodologies that exist, gross limitations may occur to the exact diagnosis of crop diseases. They are unable to achieve correct precision in disease classification, relatively lower accuracy, and delayed response time-all these obstacles result in a deficiency in effectual disease management and control. Our research proposes a new framework instigated and developed to improve crop disease detection and classification by multifaceted analysis. In the core of our methodology is the implementation of adaptive anisotropic diffusion for the denoising of obtained agro images, therefore making it a step towards assurance in data quality. Along with this is the use of a Fuzzy U-Net++ model for image segmentation, whereby fuzzy decisions in generously instill an increase in performance for image segmentation. Feature selection itself is innovated by the introduction of the Moving Gorilla Remora Algorithm (MGRA) combined with convolutional operations, setting a new benchmark in the selection of optimal features pertaining to disease identification operations. To further refine this model, classification is adeptly handled by a process inspired by the LeNet architecture, significantly improving identification against various diseases. Our approach's performance is therefore strongly assessed through a number of renowned datasets, such as PlantVillage and PlantDoc, on which test metrics show superior performance: 8.5% improvement in disease classification precision, 8.3% higher accuracy, 9.4% improved recall, with a reduction in time delay by 4.5%, area under the curve (AUC) increasing by 5.9%, a 6.5% improvement in specificity, far ahead of other methods. This work not only sets new standards in crop disease analysis but also opens possibilities for the preemptive measures to come in agricultural health, promising a future where crop management is more effective and efficient. Our results thus have implications that reach beyond the immediate benefits accruable from improved diagnosis of diseases. It is a harbinger of a new era in agricultural technology where precision, accuracy, and timeliness will meet to enhance crop resilience and yield.
农业部门面临的主要挑战在于，就现有的方法而言，在对作物病害进行准确诊断时可能会出现严重局限。它们在病害分类中无法达到正确的精度，准确率相对较低，且响应时间延迟——所有这些障碍导致有效的病害管理与控制存在不足。我们的研究提出了一个全新的框架，该框架通过多方面分析激发并开发出来，用于改进作物病害检测与分类。我们方法的核心是实施自适应各向异性扩散以对获取的农业图像进行去噪，从而朝着确保数据质量迈出了一步。与此同时，使用模糊U-Net++模型进行图像分割，其中模糊决策极大地提升了图像分割性能。特征选择本身通过引入移动大猩猩鮣算法（MGRA）并结合卷积操作进行了创新，在与病害识别操作相关的最优特征选择方面树立了新的标杆。为了进一步优化该模型，分类由受LeNet架构启发的过程巧妙处理，显著提高了对各种病害的识别能力。因此，我们的方法通过多个著名数据集（如PlantVillage和PlantDoc）进行了严格评估，测试指标显示出卓越的性能：病害分类精度提高了8.5%，准确率提高了8.3%，召回率提高了9.4%，同时时间延迟减少了4.5%，曲线下面积（AUC）增加了5.9%，特异性提高了6.5%，远远领先于其他方法。这项工作不仅在作物病害分析方面树立了新标准，还为农业健康领域未来的预防措施开辟了可能性，预示着作物管理将更加有效和高效的未来。因此，我们的结果所产生的影响超出了因改进病害诊断而直接获得的益处。它预示着农业技术新时代的到来，在这个时代，精准、准确和及时性将相互结合，以增强作物的恢复力和产量。
