Agriculture is one of the prime sources of economy and a large community is involved in cropping various plants based on the environmental conditions. However, a number of challenges are faced by the farmers including different diseases of plants. The detection and prevention of plant diseases are the serious concern and should be treated well on time for increasing the productivity. Therefore, an automated plant disease detection system can be more beneficial for monitoring the plants. Generally, the most diseases may be detected and classified from the symptoms appeared on the leaves. For the same, extraction of relevant features plays an important role. A number of methods exists to generate high dimensional features to be used in plant disease classification problem such as SPAM, CHEN, LIU, and many more. However, generated features also include unrelated and inessential features that lead to degradation in performance and computational efficiency of a classification problem. Therefore, the choice of notable features from the high dimensional feature set is required to increase the computational efficiency and accuracy of a classifier. This paper introduces a novel exponential spider monkey optimization which is employed to fix the significant features from high dimensional set of features generated by SPAM. Furthermore, the selected features are fed to support vector machine for classification of plants into diseased plants and healthy plants using some important characteristics of the leaves. The experimental outcomes illustrate that the selected features by Exponential SMO effectively increase the classification reliability of the classifier in comparison to the considered feature selection approaches.

农业是经济的主要来源之一，一个大型社区根据环境条件参与种植各种植物。但是，农民面临着许多挑战，包括不同的植物病害。植物病害的检测和预防是一个严重的问题，应该及时进行处理以提高生产率。因此，自动化的植物病害检测系统对于监测植物会更加有益。通常，大多数疾病可以从叶子上出现的症状中检测到并分类。同样，相关特征的提取也起着重要作用。存在许多生成高维特征以用于植物病害分类问题的方法，例如SPAM，CHEN，LIU等。然而，生成的特征还包括不相关且无关紧要的特征，这些特征会导致分类问题的性能和计算效率下降。因此，需要从高维特征集中选择显着特征，以提高分类器的计算效率和准确性。本文介绍了一种新颖的指数蜘蛛猴优化算法，该算法用于从SPAM生成的高维特征集中修复重要特征。此外，所选特征被馈送到支持向量机，以利用叶片的一些重要特征将植物分类为患病植物和健康植物。