Early diagnosis of leaf ailments is the most necessary and prominent way to increase agriculture production. In this paper, a computer-aided approach for classifying the ailments in plant leaf is proposed using the neutrosophic logic-based feature selection algorithm. Feature selection leads to better learning performance and lowers computational cost by choosing a small subset of features by eliminating noisy and redundant features thereby acting as a dimensionality reduction technique. Leaf disease classification is similar to other classification problems but varies significantly in the features that contribute to classification. In the proposed method, Neutrosophic Cognitive Maps (NCM) is used to select the best subsets from GLCM and statistical features that can effectively characterize the leaf ailments. Eight existing state-of-the-art feature selection techniques are compared with the proposed method in order to prove the ability of the proposed method on publicly available images from the PlantVillage repository. Further, the leaf diagnosis can be incorporated in a mobile computing system if needed using appropriate methods thereby enabling user-friendliness. The proposed feature selection method provides an overall classification accuracy of 99.8% while selecting just 11 features for leaf disease diagnosis

对叶病的早期诊断是增加农业产量的最必要和最突出的方法。本文提出了一种基于中智逻辑的特征选择算法，通过计算机辅助对植物叶片中的疾病进行分类。特征选择可通过消除噪声和冗余特征来选择较小的特征子集，从而提高学习性能，并降低计算成本，从而充当降维技术。叶片疾病分类与其他分类问题相似，但在有助于分类的特征上有很大差异。在提出的方法中，中智认知图（NCM）用于从GLCM和可以有效表征叶片疾病的统计特征中选择最佳子集。将八种现有的最先进的特征选择技术与所提出的方法进行了比较，以证明所提出的方法在PlantVillage存储库中可公开获得的图像上的能力。此外，如果需要，可以使用适当的方法将叶子诊断合并到移动计算系统中，从而实现用户友好性。提出的特征选择方法可提供99.8％的整体分类精度，而仅选择11种特征进行叶病诊断