Defect, Size, Maturity, and Quality Detection on Ladies' Finger, Bitter Gourd, and Cucumber Using Image Processing and MobileNetV2

Image processing has been proven to be an effective technique for analysis in various fields, including agriculture. In agriculture, image processing is applied to crops or the quality of products by detection, classification, and grading. With that being said, applying a deep learning algorithm to image processing for defect, size, maturity, and quality detection on ladies' fingers, bitter gourds, and cucumbers can be difficult. An expert's advice and service may often be expensive and time-consuming. Image processing techniques and a deep learning algorithm will benefit farmers by classifying the defect, size, maturity, and quality of the ladies' finger, bitter gourd, and cucumber accurately and in real-time. This paper intends to focus on the detection of the defect, size, maturity, and quality of the ladies' finger, bitter gourd, and cucumber using Watershed Algorithm, Otsu Thresholding, RGB Masking, K-means Cluster Algorithm, and MobileNetV2. Moreover, this study detected the defect, size, maturity, and quality of the vegetables correctly, and the algorithm and techniques used were effective and accurate.