Detection of plant leaf disease has been considered an interesting research field which is helpful to improve the crop and fruit yield. Computer vision and machine learning based approaches have gained huge attraction in digital image processing field. Several visual computing based techniques have been presented in the past for early prediction of plant leaf diseases. However, detection accuracy is still considered as a challenging task. Hence, in order to overcome this issue, we introduce a novel hybrid approach carried out in three forms. During the first phase, image enhancement and image conversion scheme are incorporated, which helps to overcome the low-illumination and noise related issues. In the next phase, a combined feature extraction technique is developed by using GLCM, Complex Gabor filter, Curvelet and image moments. Finally, a Neuro-Fuzzy Logic classifier is trained with the extracted features. The proposed approach is implemented using MATLAB simulation tool where PlantVillage Database is considered for analysis. The average detection accuracy has been obtained as more than 90% for 2 test cases which shows that the proposed combination of feature extraction and image pre-processing process is able to obtain improved classification accuracy. This work is useful for the students of UG/PG programme to carry out Project-based learning.

植物叶病的检测已被认为是一个有趣的研究领域，有助于提高作物和水果的产量。基于计算机视觉和机器学习的方法已在数字图像处理领域获得了巨大的吸引力。过去已经提出了几种基于视觉计算的技术，用于植物叶病的早期预测。但是，检测精度仍然被认为是一项艰巨的任务。因此，为了克服这个问题，我们介绍了一种以三种形式进行的新颖混合方法。在第一阶段，将图像增强和图像转换方案结合在一起，这有助于克服与​​低照度和噪声相关的问题。在下一阶段，将通过使用GLCM，Complex Gabor滤波器，Curvelet和图像矩来开发组合特征提取技术。最后，使用提取的特征训练神经模糊逻辑分类器。所提出的方法是使用MATLAB仿真工具实现的，其中考虑了PlantVillage数据库进行分析。对于2个测试用例，平均检测精度已达到90％以上，这表明所提出的特征提取和图像预处理过程的组合能够获得改进的分类精度。这项工作对于UG / PG计划的学生进行基于项目的学习很有帮助。对于2个测试用例，平均检测精度已达到90％以上，这表明所提出的特征提取和图像预处理过程的组合能够获得改进的分类精度。这项工作对于UG / PG计划的学生进行基于项目的学习很有帮助。对于2个测试用例，平均检测精度已达到90％以上，这表明所提出的特征提取和图像预处理过程的组合能够获得改进的分类精度。这项工作对于UG / PG计划的学生进行基于项目的学习很有帮助。