# Automatic Detection of Tomato Diseases Using Deep Transfer Learning
###### Abstract
[22] presented spectral vegetation indices as features for classification using K-nearest neighbors (KNN), and they reported a 100% accuracy, albeit with a very small dataset (445 images). This work is motivated by the following factors:  
* The adoption and implementation of technological innovations is generally lacking in the agricultural literature in comparison with other fields (e.g., medicine). This is especially true for the number of artificial intelligence applications in agriculture versus in medicine. * Traditional classification methods rely upon explicit feature extraction and/or image processing techniques, which may be sensitive to changes in image quality, orientation, size, lighting, noise, etc. Furthermore, the classification performance is directly affected by the quality of features on which it is based. Moreover, pre-processing increases the delay, computational requirements, and compounded errors. In addition, it may hinder the deployment of real-life applications if complicated actions are required by the user. * Previous works suffer from several deficiencies. First, some of these studies artificially increase the size of the dataset by including subtle differences in the dataset images. However, deep learning models are known to be immune to such changes. This duplication artificially improves the results by exposing the model to recognizing the similarities with the original images rather than features of the disease or health states. Second, building a customized CNN model is fraught with risks in terms of overfitting, underfitting, efficiency, and hardware requirements. Using deep transfer learning with pre-existing network architecture, carries with it the inherent credibility of the thousands of applications based on these models and the extensive scrutiny they have gone through, although at the expense of perceived lack of novelty and originality. Third, transfer learning is able to achieve competitive if not superior performance. In this paper, deep transfer learning was used to detect and classify tomato disease using images of infected leaves. This approach has the advantages of employingwell-established, trustworthy, and robust models without the need to redesign/reinvent a custom architecture. Moreover, deep learning models can render feature extraction and image preprocessing needless. The contributions of this paper are as follows:  
1. Develop deep transfer learning models for the detection and classification of tomato diseases from leaf images for nine tomato diseases: bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus. In addition, healthy leaves were discerned as a 10 th class;
2. Implement transfer learning of eleven deep convolutional neural networks models for the classification of leaf images into ten classes. Future Implementation of such a system in smart devices will greatly help farmers do prompt disease control;
3. Evaluate the performance of the various models using multiple metrics that cover many aspects of the detection and classification capabilities. Moreover, the training and validation times were reported. The remainder of this paper is organized as follows: the data, convolutional network models, and performance evaluation metrics and setup are presented in detail in Section 2, Section 3 discusses the performance evaluation results along with comparison to the related literature and discussion of the models, and we conclude in Section 4.