Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers

Autor: Eduardo Jr Piedad, Tuan-Tang Le, Chyi-Yeu Lin
Rok vydání: 2019
Předmět:
Zdroj: Postharvest Biology and Technology. 156:110922
ISSN: 0925-5214
DOI: 10.1016/j.postharvbio.2019.05.023
Popis: Practical classification of some horticultural crops such as banana tiers, lanzones and grapes come into clusters instead of individual classification. Unlike most of classification studies, clustered crops are rarely studied due to their complex physical structure. A noninvasive deep learning classification of clustered banana given only a single image feature has been developed as a pioneering deep learning study for clustered horticultural crops. In recent deep learning developments, mask region-based convolution neural networks, also known as Mask R-CNN, show unique applications in image recognition by detecting objects within an image while simultaneously generating segmentation masks. With Mask R-CNN, detection of the complex banana fruit within an image predicts the banana class while at the same time generating a mask separating the fruit from its background. A real dataset is used based on banana tiers and the developed model discriminates normal from abnormal tiers. Unlike the previous general machine learning study, which discriminates reject class from normal class with classification accuracy of 79%, our deep learning model obtained a better averaged accuracy of 92.5%. The previous average weighted accuracy of 94.2% also improved to 96.1% with only a single image feature instead of tedious multiple image and size features. With data augmentation, the model slightly improved into 93.8% accuracy on classifying reject class and 96.5% for overall accuracy. Having successfully implemented in banana tiers, this deep learning classification can also serve as basis for other clustered horticultural crops.
Databáze: OpenAIRE
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