![]() In: Proceedings of the 2018 VII International Conference on Network, Communication and Computing, pp. IEEE (2016)īaculo, M.J.C., Marcos, N.: Automatic mango detection using image processing and HOG-SVM. In: 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), pp. Īshok, V., Vinod, D.: A comparative study of feature extraction methods in defect classification of mangoes using neural network. 79–88 (2017)Īnurekha, D., Sankaran, R.A.: Efficient classification and grading of MANGOES with GANFIS for improved performance. KeywordsĪmara, J., Bouaziz, B., Algergawy, A., et al.: A deep learning-based approach for banana leaf diseases classification. Results show that Faster R-CNN achieved the highest average precision of 0.901 at \(aP_\). The results of the experiments show comparable performance between the modified and existing state-of-the-art object detection frameworks. A dataset consisting of 1329 cecid fly surface blemishes was used to train the object detection models. ![]() This paper also proposes modified versions of R-CNN and FR-CNN replacing its region search algorithms with segmentation-based region extraction. Object detection frameworks using CNN were used to localize and detect multiple defects present in a single mango image. This paper investigates the automated detection of a mango defect caused by cecid flies, which can affect a significant portion of the production yield. Mango export has experienced rapid growth in global trade over the past few years, however, they are susceptible to surface defects that can affect their market value. ![]()
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