Data-driven Smart Farming to Grade and Classify Tomatoes using CNN and FFNN for Agricultural Innovation - Joshi, Binisha; Konda, Bikal; Karmacharya, Rajan (SXC Journal, 2024)
Identifying images poses a challenge in computer vision, but the use of deep learning methods has greatly enhanced the performance of image classification systems. In this research, Convolutional Neural Networks (CNN) and Feed Forward Neural Networks (FFNN) have been utilized for image classification. CNN is extremely effective in picture classification, which extracts relevant information from images using convolutional and pooling layers to minimize the dimensionality of the derived features, while FFNN algorithm is a classic neural network with fully linked layers. It can be used to further process the features extracted by CNN. The study makes use of CNN and FFNN models to train a huge dataset of tomato images to categorize them based on their type, ripeness, and damage status. CNN is found to be more effective in the case of tomato classification as compared to FFNN algorithm in all the use cases. The accuracy for classification of an image (tomato or not) using CNN is 95.83%, type classification using CNN is 81.52%, whereas using FFNN is 66.30%; ripeness grading for CNN is 92.86%, whereas for FFNN it is 57.14%; and damage status grading is 92.86% using CNN and 67.86% using FFNN. Therefore, it can be concluded that quality processing of tomatoes can be improved using CNN.