Crop identification and disease classification using traditional machine learning and deep learning approaches
Crop and disease classification is one of the important problems in automation of agricultural processes with multi-cropping method where the field is cultivated with more than one crop. In order to solve this classification problem, a study has been carried out in the field cultivating eggplant (Solanum melongena) and tomato (Solanum lycopersicum) using the images obtained from a mobile phone camera. Textural descriptors namely contrast, correlation, energy and homogeneity were extracted from the gray-scale converted RGB image for crop identification, i.e., (tomato or eggplant) and the same descriptors were extracted from the gray-scale converted image from Hue Saturation Value (HSV) for disease classification (due to Cercospora leaf spot disease or two-spotted spider infestation). Discriminant analysis, Naive Bayes algorithm, support vector machine and neural network were the classification algorithms used with a resulting best accuracy of 97.61%, 95.62%, 98.01% and 98.94% for crop identification, 86.09%, 76.52%, 86.96% and 86.04% for disease classification respectively. Similarly, application of algorithm with 6 histogram-based descriptors for health status detection resulted in an accuracy of 66.67%, 37.04%, 50% and 72.9% respectively. Deep learning algorithm namely AlexNet was also evaluated which resulted in an accuracy of 100% for crop identification, 89.36% for health status detection and 81.51% for disease classification. Among the algorithms, AlexNet resulted in the best average accuracy of 90.29% for the above classification tasks.