Abstract
Tomatoes form a key ingredient in Indian cuisines. Hence it’s vital to enhance the productivity of tomatoes. This is done by introducing automation in the agricultural sector. Automation not only increases the productivity but also improves the quality. Automation in agriculture makes use of computer vision to detect tomatoes. In this paper, a computer vision system is developed to identify and classify the various stages of maturity in a tomato using deep learning. A database to train the deep learning model is prepared by collecting images of tomatoes displaying various stages of maturity. Images were collected not only based on maturity of tomato but also based on the way tomatoes were located on the plant (single tomato, adjacent tomatoes, tomato partially shaded by leaves). Here we have evaluated three neural network architectures namely AlexNet, GoogLeNet and ResNet-50. Based on the evaluation, it was concluded that ResNet-50 performs better in terms of training time and accuracy. The developed system comprises of ResNet-50 integrated with YOLO v2 and is tested in real time. The accuracy rate for detection of tomatoes in real time using the proposed system is 90%.
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Mulla, N.A., Ravichandran, S., Naik, P.K., Balappa, B.U. (2022). Computer Vision System to Detect Maturity of Tomatoes in Real Time Using Deep Learning. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_48
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DOI: https://doi.org/10.1007/978-981-16-4807-6_48
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