# Automatic Detection of Tomato Diseases Using Deep Transfer Learning
###### Abstract  
Global food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing economical loss and boost output. Technological innovations have great potential in facilitating disease detection and control. More specifically, artificial intelligence algorithms in the form of deep learning methods have established themselves in many real-life applications in a wide range of disciplines (e.g., medicine, agriculture, or facial recognition, etc.). In this paper, we aim at applying deep transfer learning in the classification of nine tomato diseases (i.e., bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus) in addition to the healthy state. The approach in this work uses leaf images as input, which is fed to convolutional neural network models. No preprocessing, feature extraction, or image processing is required. Moreover, the models are based on transfer learning of well-established deep learning networks. The performance was extensively evaluated using multiple strategies for data split and a number of metrics. In addition, the experiments were repeated 10 times to account for randomness. The ten categories were classified with mean values of 99.3% precision, 99.2% F1 score, 99.1% recall, and 99.4% accuracy. Such results show that it is highly feasible to develop smartphone-based applications that can aid plant pathologists and farmers to quickly and accurately perform disease detection and subsequent control. deep learning: tomatoes; virus; bacteria; blight; spot; mold; image classification; artificial intelligence +
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swath of applications from various disciplines. AI systems capture domain knowledge in their models through the training and validation process. They provide decision-making capabilities with nontrivial sophistication and complexity [6,7]. More specifically, deep learning algorithms have enabled the capture of intricate relationships and features of real-life processes. Convolutional neural networks (CNNs) are one of the types of deep learning algorithms that were found to be particularly useful for direct image-based decision-making and objection detection [8]. Neural networks are comprised of three layers; input, output, and hidden. On the other hand, deep learning involves a far greater number of layers, which enables the capturing of input features and details at various scales. Out of the many deep learning artificial intelligence algorithms, convolutional neural networks are the most suitable for handling images as input [8]. Layers in a convolutional neural network perform a series of convolution operations using filters of various sizes, which is typically followed by a rectified linear unit (ReLU) activation function. The result from the ReLU is a feature map that is downsampled by the subsequent pooling layer. In general, the final layer before the output in CNN is a fully connected layer, which combines the various features learned from previous layers and feeds the output layer. Building CNN models is an elaborate process, which needs to balance the computational cost with the ability to automatically extract appropriate features at various scales, orientations, colors, reflections, and spatial properties. Moreover, the models may suffer from overfitting, underfitting, or inefficiency. In addition, thorough evaluation is needed to establish the trustworthiness of the models. Luckily, several public and well-established models exist in the literature. These models offer a wide-range of reliable capabilities with great efficiency [9]. Importantly, these models can be reused via an approach called transfer learning.