FRUIT RIPENESS DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Fruit ripeness detection is crucial in the agriculture field to determine the time of harvest and fruit quality. Delays or errors in timing can lead to significant loss in crops, 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 using machine learning and computer vision technologies. This system will use the CNN algorithm to identify the fruits and classify its ripeness 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 increase the size of the dataset 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.