Abstract
Early diagnosis and accurate identification to tomato leaf diseases contribute on controlling the diffusion of infection and guarantee healthy to the plant which in role result in increasing the crop harvest. Nine common types of tomato leaf diseases have a great effect on the quality and quantity of tomato crop yield. The tradition approaches of features extraction and image classification cannot ensure a high accuracy rate for leaf diseases identification. This paper suggests an automatic detection approach for tomato leaf diseases based on the fine tuning and transfer learning to the pre-trained of deep Convolutional Neural Networks. Three pre-trained deep networks based on transfer learning: AlexNet, VGG-16 Net and SqueezeNet are suggested for their performances analysis in tomato leaf diseases classification. The proposed networks are carried out on two different dataset, one of them is a small dataset using only four different diseases while the other is a large dataset of leaves accompanied with symptoms of nine diseases and healthy leaves. The performance of the suggested networks is evaluated in terms of classification accuracy and the elapsed time during their training. The performance of the suggested networks using the small dataset are also compared with that of the-state-of-the-art technique in literature. The experimental results with the small dataset demonstrate that the accuracy of classification of the suggested networks outperform by 8.1% and 15% over the classification accuracy of the technique in literature. On other side when using the large dataset, the proposed pre-trained AlexNet achieves high classification accuracy by 97.4% and the consuming time during its training is lower than those of the other pre-trained networks. Generally, it can be concluded that AlexNet has outstanding performance for diagnosing the tomato leaf diseases in terms of accuracy and execution time compared to the other networks. On contrary, the performance of VGG-16 Net in metric of classification accuracy is the best yet the largest consuming time among other networks.
Highlights
In the past decades, the plant diseases identification were mostly performed through the optical observation by farmer
The suggested networks Alex, Squeeze and VGG-16 are trained with the following tomato leaf diseases: Bacteria Spot (BS), Late Blight (LB), Spetoria
The authors in this paper presented a Convolutional Neural Network model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification
Summary
The plant diseases identification were mostly performed through the optical observation by farmer. Machine learning techniques has been emerged as an intelligent technique to be used in large scale of this field They were applied in early stage of plant diseases diagnosis and classification. Athanikar and Badar [3] implemented Neural Network to classify the potato leaf image into category of healthy and diseased Their results demonstrated that BPNN could effectively detect the spots leaf disease and could categorized the disease type with accuracy 92%. More studies of using CNN in the field of crop disease recognition and identification as a new hot spot research in agricultural field were presented in [15,16,17,18,19,20,21,22,23]. In this paper three pre-trained deep networks based on the transfer learning and fine tuning are suggested for tomato leaf diseases classification.
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