Grape detection with convolutional neural networks

Grape detection with convolutional neural networks

Convolutional neural networks, as a type of deep learning approach, have revolutionized the field of computer vision and pattern recognition through state of the art performance in a large number of classification tasks. Machine learning has been recently incorporated into intelligent systems related to agricultural and food production to decrease manual processing when dealing with large number of operations. Feedforward artificial neural networks such as convolutional neural networks can be used in agriculture for the segmentation and classification of images containing objects of interests such as fruits, or leaves. It is however unknown what is the best architecture to use, if it is necessary to propose new architectures, and what is the impact of the input feature space on the classification performance. In this paper, we propose to detect two types of grapes (Albariño white grapes and Barbera red grapes) in images. We investigate 1) the impact of the input feature space: color images, grayscale images, and color histograms using convolutional neural networks; 2) the impact of the parameters such as the size of the blocks, and the impact of data augmentation; 3) the performance of 11 pre-trained deep learning architectures, i.e. using a transfer learning approach for the classification. The results support the conclusion that images of grapes can be efficiently segmented using different feature spaces where color images provide the best performance. With convolutional neural networks using transfer learning, the best performance is achieved with Resnet networks reaching an accuracy of 99% for both red and white grapes. Finally, data augmentation, image normalization, and the input feature space have a key impact on the overall performance.

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