Tomato Disease Recognition Using a Compact Convolutional Neural Network

Detection of the diseases on tomatoes in advance and making early intervention treating increases production amount, efficiency quality which will satisfy consumer with a more affordable shelf price. In this way, efforts farmers who are waiting for harvest throughout season not be wasted. paper compact convolutional neural network (CNN) is proposed identification task where comprised only 6 layers that why it computationally cheap terms parameters employed network. This trained by using PlantVillage’s tomato crops dataset consists 10 classes (9 1 healthy). The first compared well-known pre-trained ImageNet deep networks transfer learning approach. results show performed better than knowledge transferred models shown there no need to constitute very large, complicated architectures achieve superior performance. Furthermore, increase performance network, data augmentation techniques also during training. achieves an accuracy, F1 score, Matthews correlation coefficient, true positive rate negative 99.70%, 98.49%, 98.31%, 98.49% 99.81%, respectively 9,077 unseen test images. Our or similar state-of-the-art approaches used PlantVillage database method employs cheapest architecture.