Machine vision-based expert system for automated cucumber diseases recognition and classification

Automated cucumber disease detection may significantly provide agricultural assistance for remote farmers. Due to having the similarity symptoms, it is challenging to differentiate between various forms of cucumber disease. This paper proposes an automated solution to recognize and classify the cucumber disease using different computer vision-based techniques. In light of this circumstance, we design a computerized cucumber disease recognition system that analyzes images collected by mobile phones and can recognize diseases to assist rural farmers in dealing with the situation. In our method, a discriminating feature set is initially extracted from the input images. Then, K-means clustering segmentation separates the disease-affected regions from the remaining image part. Finally, the diseases are classified using five different classification algorithms. Different evaluation metrics, including accuracy, precision, sensitivity, specificity, False-Positive Rate (FPR), False-Negative Rate (FNR), are used to analyze the classifier's performance. We have carried out several experiments to illustrate the use of the proposed expert system. Our experiments showed that random forest exceeds all other classifiers regarding the total number of metrics used, with an accuracy of 85.84% on our dataset.