The Model for The Classification of The Ripeness Stage of Pomegranate Fruits In Orchards Using

Abstract

\n In agriculture, a timely and accurate estimate of ripeness in the orchard improves the post-harvest process. Choosing fruits based on their maturity stages can reduce storage costs and increase market results. In addition, the estimation of the ripeness of the fruit based on the detection of input and output indicators has brought about practical effects in the harvesting process, as well as determining the amount of water needed for irrigation. pepper, the amount of fertilizer for the end of the season appropriate. In this paper, propose a technical solution for a model to detect persimmon green grapefruit fruit at agricultural farms, Vietnam. Aggregation model and transfer learning method are used. The proposed model contains two object detection sub models and the decision model is the pre-processed model, the transfer model and the corresponding aggregation model. Improving the YOLO algorithm is trained with more than one hundred object types, the total proposed processing is 500,000 images, from the COCO image data set used as a preprocessing model. Aggregation model and transfer learning method are also used as an initial step to train the model transferred by the transfer learning technique. Only images are used for transfer model training. Finally, the aggregation model with the techniques used to make decisions selects the best results from the pre-trained model and the transfer model. Using our proposed model, it has improved and reduced the time when analyzing the maximum number of training data sets and training time. The accuracy of model union is 98.20%. The test results of the classifier are proposed through a data set of 10000 images of each layer for sensitivity of 98.2%, specificity 97.2% with accuracy of 96.5% and 0, 98 in training for all grades.