Automatic Identification of banana quality with Deep Neural Network Classification (DNN)
Abstract
The industrialization of agriculture has gradually taken place in Colombia, because of the need to optimize the time and effort required for the processes involved from growing fruits and vegetables to their distribution and commercialization, whether they are meant for exportation or commercialization within the country. Fruit classification is a very important process before they are being taken out as a final product, since predetermined guidelines issued by regulatory entities that define the quality of the harvested product must be followed. The authors propose a low-cost prototype for the automatic classification of bananas according to the NTC 1190 standard (Colombian normative), using a convolutional neural network (CNN) of MobileNetV2 architecture trained through transfer learning and implemented in a Raspberry Pi 3B+ with a camera to monitor the specimens and an easy interface for interaction with the user, as well as a case designed to contain the hardware and allow access to its ports in the most compact way possible. The datasets utilized in this work for training, validation, and testing, consists of images taken from two free access fruit database and others acquired by the researchers. The achieved precision is 87 %, enough to ensure reliability and low computational cost.
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References
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