Abstract
Accurately recognizing rotten fruits has been important, particularly in the agricultural sector. Generally, manual efforts are utilized to classify fresh and rotting fruits which can be tedious sometimes. Unlike humans, machines do not get tired from performing the same task repeatedly. This study suggested a technique for detecting flaws in fruit images, which might reduce human effort, slash manufacturing costs and save time. The rotten fruits may contaminate the good fruits if the flaws are not found within time. Therefore, to prevent the spread of rottenness, a model has been put forward. Based on the fruit images provided as input, the recommended system can identify rotten and fresh fruits. Images of oranges, bananas, and apples have been considered in this paper. Using a convolutional neural network along with max pooling and MobileNetV2 architecture, the features from the input images are collected, based on which images are subsequently classified. On a Kaggle dataset, the suggested model’s performance is evaluated, and by using MobileNetV2, it gets the greatest accuracy in training data (99.56%) and validation set (99.69%). The max pooling had a validation accuracy of 95.01% and a training accuracy of 94.97%. According to the results, the suggested CNN model can differentiate between fresh and rotten apples in an efficient manner.
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Singh, A., Gupta, R., Kumar, A. (2023). Fresh and Rotten Fruit Detection Using Deep CNN and MobileNetV2. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_22
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