Decision-making support system for fruit diseases classification using Deep Learning

Fruit diseases are a continuous hazard to farmers. By applying computer vision-based techniques, precision agriculture can support the farmers in the decision making for fruit disease control. Features extraction is an essential task for the computer vision pipeline. Nowadays, in general, feature extraction for fruit diseases are handcrafted. However, empirical results in different domains confirm that features learned by Convolutional neural networks (CNNs) provide significant improvements in accuracy over handcrafted features. CNNs have been applied in many computer vision tasks, replacing the hand-engineered models. In general, a large-scale image dataset is necessary for training a CNN. However, there are not many fruit disease images available to compose the dataset. We propose to train a tiny and efficient deep convolutional network developed to run in the mobile devices to classify healthy peach fruits and three peach diseases. Based on transfer learning techniques and data augmentation strategies, the proposed model achieves a Macroaverage F1-score of 0.96. The model does not misclassify any disease class. This achievement shows the potential of using small CNN models for fruit disease classification when having a small quantity of training data.