An evaluation of various machine learning approaches for detecting leaf diseases in agriculture

Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques.

To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning.

The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.