Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks

Quality assessment of agricultural products is one of the most important factors in promoting their marketability and waste control management. Image processing systems are new and non-destructive methods that have various applications in the agriculture sector, including product grading. The purpose of this study is to use an improved CNN algorithm to detect the apparent defects of sour lemon fruit, grade them and provide an efficient system to do so. In order to identify and categorize defects, sour lemon images were prepared and placed in two groups of healthy and damaged ones. After pre-processing, the images were categorized based on an improved algorithm (CNN). From the data augmentation and the stochastic pooling mechanism were used to improve CNN results. In addition, to compare the proposed model with other methods, feature extraction algorithms (histogram of oriented gradients (HOG) and local binary patterns (LBP)) and k-nearest neighbour (KNN), artifical neural network (ANN), Fuzzy, support vector machine (SVM) and decision tree (DT) classification algorithms were used. The results showed that the accuracy of the convolutional neural network (CNN) was 100 %. Therefore, it can be said that the CNN method and image processing are effective in managing waste and promoting the traditional method of sour lemon grading.