International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
Identification of Tomato Leaf Disease Using Convolutional Neural Networks UNet 1Ankush Nandi, 2G.V. Anil, 3K. Gnana Likhitha, 4Ashmi Raj
1,2,3,4Student, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract ‐ Plants play a crucial part in preserving the ecosystem's equilibrium and supplying the resources required for human survival. Tomatoes, scientifically known as Solanum lycopersicum, are among the most widely cultivated and consumed vegetables globally. They are a good source of potassium, folate, and vitamin C, among other important minerals and vitamins. This nutritional value makes them a valuable crop for ensuring food security and promoting a healthy diet. Despite their significance, tomato crops face several challenges, with diseases being one of the major issues. The emergence of diseases can lead to substantial yield losses and quality deterioration, posing a significant threat to both local and international tomato markets. Timely and accurate disease detection is crucial to mitigate these losses and maintain the economic viability of tomato cultivation. Our research paper addresses the crucial problem of tomato leaf disease detection in this particular context. To automatically identify tomato leaf diseases like bacterial spot, late blight, early blight, spider mites, mosaic virus, yellow leaf virus, target spot, and septoria leaf spot, we suggested a novel method. utilizing the U-Net architecture and convolutional neural networks (CNN). Our study makes use of a dataset that we gathered from multiple sources that includes pictures of both healthy and unhealthy tomato leaves. Based on the research, when compared with VGG16, VGG19, CNN achieved higher accuracy. So, we are trying to employ a CNN for feature extraction and a U-Net for pixel-wise segmentation, allowing us to pinpoint disease-affected areas with high precision. We anticipate that the suggested method's processing time will be much less than that of these models.
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The field of agriculture could be strengthened by automating the detection of leaf disease using image processing techniques like deep learning and machine learning, which could yield quick and accurate results. These methods entail a number of processes, such as feature extraction, machine learning, segmentation, processing, and image acquisition. Plant leaf diseases can be identified and categorized from visuals using machine learning techniques. Accurate disease detection and classification are made possible by the utilization of machine learning algorithms to extract features from which patterns and relationships can be learned Machine learning is used to identify and classify various fungal, bacterial, and viral diseases in plant leaves. These diseases can significantly affect crop yield and quality. Different machine learning algorithms are employed for disease classification. These comprise artificial neural networks (an ANN), support vector machines, and deep learning models such as GoogLeNet and the network CNN. SVM is used to classify diseases into multiple classes using the features that are extracted. RGB images of leaves are used as data sources for machine learning. These images contain visual information about the symptoms and characteristics of the diseases. Deep learning models are trained to automatically learn relevant features from the data, which can be particularly effective for image-based tasks. Deep learning techniques, such as convolutional neural networks, have shown excellent performances in various image analysis tasks, including leaf segmentation, leaf spot detection, and disease classification. CNNs are designed to automatically learn hierarchical features from images. They consist of multiple layers, each responsible for detecting increasingly complex patterns. This hierarchical feature learning allows CNNs to capture relevant information at different scales, making them highly effective in recognizing intricate details in images. Because CNNs are translationinvariant, they can recognize features no matter where they are in the picture. This property is especially valuable when dealing with images of plant leaves, where the position and orientation of disease symptoms may vary. Ferentinos developed CNN models to identify and diagnose plant
Introduction
For thousands of years, agriculture has been the main source of human survival. Every year, plant diseases cause significant losses in crop productivity across the world. A vital role in the economy is played by agriculture, the threat of crop diseases casts a long shadow over both crop quality and quantity, but they are prone to a number of illnesses that could seriously harm them and reduce their productivity. The prompt identification and precise diagnosis of diseases affecting plant leaves is one of the main obstacles in the management of plant diseases.
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