• Korgaonkar Urvi Rajesh
• Badshah Pakhi Ravi
• Achu Sheri Jeeju
• Menon Deepa Rajagopalan
• Payel Thakur

Fruit ripeness detection is crucial in the agriculture field to determine the time of harvest and fruit quality. Substantial crop loss might result from timing slips or delays, consequently affecting income rates. Manual detection of ripeness in fruits can be inefficient for large scale implementation and pose issues such as requiring lots of time and intensive labor. Hence, in this project, fruit ripeness is detected utilizing computer vision and machine learning technologies. The method being used will recognize the fruits and categorize their ripeness using the CNN algorithm, on the basis of the dataset it is trained on. Our system will detect ripeness of 6 different types of fruits. Data augmentation techniques are used to expand the dataset's size prior to building a trained model. This technology can be integrated with robotics to implement smart agriculture and help farmers view their crops in real time, know the number of mature fruits, plan harvesting and assist in fruit picking and sorting.

Fruit Ripeness, Convolutional Neural Network, Machine Learning

"FRUIT RIPENESS DETECTION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.f557-f562, April-2023, Available :http://www.jetir.org/papers/JETIR2304573.pdf

"FRUIT RIPENESS DETECTION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppf557-f562, April-2023, Available at : http://www.jetir.org/papers/JETIR2304573.pdf