Detection of Diseases in Tomato Plant using Machine Learning
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Abstract
A major part of the Indian economy relies on agriculture, thus identification of any diseased crop in the initial phase is very important as these diseases cause a significant drop in agricultural production and also affect the economy of the country. Tomato crops are susceptible to various diseases which may be caused due to transmission of diseases through Air or Soil. We have tried to automate the procedure of detection of diseases in the Tomato Plant by studying several attributes related to the leaf of the plant. Using various machine learning algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), ResNet, and InceptionV3 we have trained the model, and based on the results obtained we have evaluated and compared the performance of these algorithms on different features set. For the dataset we had 10 classes (healthy and other unhealthy classes) having a total of 18,450 images for the training of the models. After implementing all of the algorithms and comparing their results we found that the ResNet was most appropriate for extracting distinct attributes from images. The trained models can be used to detect diseases in Tomato Plant timely and automatically.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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