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Journal articles on the topic 'Tomato Leaf Disease'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Pore, Prof Yogita, Suraj Teli, Swaraj Ghuge, and Nikhil Patil. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1767–70. http://dx.doi.org/10.22214/ijraset.2023.51405.

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Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that displa

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M, Megha. "Tomato Leaf Disease Detection and Monitoring System." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1746–49. http://dx.doi.org/10.21275/sr22719075340.

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M. R., Dr Sanghavi. "Tomato Leaf Disease Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33787.

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Tomato is a widely cultivated crop with significant economic importance in the agro based industry. However, tomato plants are susceptible to various diseases that can severely impact yield and quality. Early and accurate detection of these diseases is crucial for effective disease management and ensuring optimal production. In this study, we propose a novel approach that a convolutional Neural Network (CNN) for the automated detection of tomato leaf diseases. First, Convolutions is employed to reduce the dimensionality of the input data, extracting the most relevant features for disease detec

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Jasani, Abhishek, Mehul Dholi, and Soham Purkar. "Tomato Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 918–22. http://dx.doi.org/10.22214/ijraset.2022.41918.

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Abstract: Tomato is an important crop in India and affects India’s economy in many ways. It is observed that the development in agriculture is sluggish nowadays due to the attack of diseases. Many farmers detect diseases by their previous experience or some take help from experts. Traditional ways are often used to detect the diseases by the farmers. So, there is the possibility of an inaccurate diagnosis of diseases having very large similarity in their symptoms. So, it is essential to move towards the new strategies for automatic diagnosis and controlling of disease. So, there is a need for

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Roopa, Ms, and Ayush C. "Tomato Leaf Disease Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem.spejss003.

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Tomato plants are particularly vulnerable to leaf diseases, which can take a serious toll on crop yield and quality if not caught early. Traditionally, farmers and agricultural experts rely on manual inspection to spot these diseases a method that can be both slow and prone to mistakes. To streamline this process, our project introduces an automated system that uses machine learning and image processing to detect tomato leaf diseases more accurately and efficiently. We worked with a dataset of tomato leaf images that includes both healthy leaves and those affected by diseases like Early Blight

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Singh, Ganesh Bahadur, Rajneesh Rani, Nonita Sharma, and Deepti Kakkar. "Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks." International Journal of Agricultural and Environmental Information Systems 12, no. 4 (2021): 1–22. http://dx.doi.org/10.4018/ijaeis.20211001.oa3.

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Crop disease is a major issue now days; as it drastically reduces food production rate. Tomato is cultivated in major part of the world. The most common diseases that affect tomato crops are bacterial spot, early blight, septoria leaf spot, late blight, leaf mold, target spot, etc. In order to increase the production rate of tomato, early identification of diseases is highly required. The existing work contains very less accurate system for identification of tomato crop diseases. The goal of our work is to propose cost effective and efficient deep learning model inspired from Alexnet for ident

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Madderi, Sivalingam Saravanan, and Lakshmi Devi Badabagni. "Encouraging hygiene permanence in tomato leaf and applying machine learning technique." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 343–49. https://doi.org/10.11591/ijeecs.v33.i1.pp343-349.

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Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study,

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Madderi Sivalingam, Saravanan, and Lakshmi Devi Badabagni. "Encouraging hygiene permanence in tomato leaf and applying machine learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 343. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp343-349.

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<div align="center"><span>Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it i

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Nagaveni, B. Biradar, P. Farida, Yashoda, R. Sneha, and T. Tejeshwari. "Tomato Leaf Disease Detection Using Deep Learning and Flask." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 4 (2025): 23–27. https://doi.org/10.5281/zenodo.15244627.

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Tomato plants are vulnerable to various leaf diseases that reduce crop yield and quality. This project uses deep  learning, specifically a Convolutional Neural Network (CNN), to detect and classify tomato leaf diseases accurately.  A dataset containing images of healthy and diseased leaves is used for training and validation. The model is deployed  via a Flask web application, enabling users to upload leaf images and receive instant diagnosis. This system provides  an accessible and cost-effective tool for early disease detection. It aims to support farmers and improve toma

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Chopra, Gaurav, and Pawan Whig. "Analysis of Tomato Leaf Disease Identification Techniques." Journal of Computer Science and Engineering (JCSE) 2, no. 2 (2021): 98–103. http://dx.doi.org/10.36596/jcse.v2i2.171.

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India loses thousands of metric tons of tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses to farmers and possess a threat to the agriculture sector. Understanding how does an algorithm learn to classify different types of tomato leaf disease will help scientist and engineers built accurate models for tomato leaf disease detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant diseases. However, human benchmarks in diagnosing plant disease have still not be

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Vengaiah, Cheemaladinne, and Srinivasa Reddy Konda. "A Review on Tomato Leaf Disease Detection using Deep Learning Approaches." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 647–64. http://dx.doi.org/10.17762/ijritcc.v11i9s.7479.

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Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomato

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Saxena, Niharika, and Dr Neha Sharma. "IDENTIFICATION OF TOMATO LEAF DISEASE PREDICTION USING CNN." International Journal of Engineering Science Technologies 6, no. 5 (2022): 46–58. http://dx.doi.org/10.29121/ijoest.v6.i5.2022.397.

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In India tomatoes are broadly vegetable crop. However, the tropical environment is ideal for tomato plant growth, specific climatic conditions and other factors influence tomato plant growth. Aside from these environmental factors and natural disasters, plant disease is a serious agricultural production issue that causes economic loss. As an outcome, early illness detection may produce better results than existing detection methods. As a result, deep learning approaches based on computer vision might be used to detect diseases early. The disease categorization and detection strategies used to

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Islam, Mohammad Nurul, Aneesa Ansari, and RH Sarker. "Tomato leaf curl Patna virus causing tomato leaf curl disease in Bangladesh." Bangladesh Journal of Botany 48, no. 1 (2019): 153–61. http://dx.doi.org/10.3329/bjb.v48i1.47434.

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Tomato leaf curl virus (ToLCV) has appeared as a potential threat to the tomato production in the world. ToLCV, a member of the family Geminiviridae may contain either bipartite or monopartite genome. The genetic nature of a monopartite ToLCV isolate characterized from the tomato leaf curl diseased samples of Jamalpur district, Bangladesh (ToLCV-JB) has been reported. The products of rolling circle amplification (RCA) were digested, cloned and sequenced. Sequence analysis revealed the features of begomovirus genome organization in the ToLCV-JB isolate, containing six open reading frames. BLAST

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Korade, Nilesh B., Mahendra B. Salunke, Amol A. Bhosle, et al. "Tomato Leaf Disease Detection with YOLOV8 Leaf Extraction, Resnet-50 Classification, and Gpt-3.5 for Treatment Recommendations." International Research Journal of Multidisciplinary Scope 06, no. 01 (2025): 879–91. https://doi.org/10.47857/irjms.2025.v06i01.02864.

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India produces 20 million metric tons of tomatoes annually, with 150,000 metric tons being exported to international markets. India ranks as the leading producer and exporter of tomatoes globally, and tomato farming has a significant contribution to India's agricultural economy, with millions of farmers relying on tomato farming for their livelihood. Tomatoes are in high demand during the summer, but cultivating them at this time of year is challenging because the hot climate increases the susceptibility to numerous diseases. In this study, we collected 5250 images of tomato leaves suffering f

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Saxena, Niharika, and Neha Sharma. "TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING." International Journal of Engineering Technologies and Management Research 9, no. 6 (2022): 1–14. http://dx.doi.org/10.29121/ijetmr.v9.i6.2022.1177.

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Tomatoes are the most extensively planted vegetable crop in India's agricultural lands. Although the tropical environment is favorable for its growth, specific climatic conditions and other variables influence tomato plant growth. In addition to these environmental circumstances and natural disasters, plant disease is a severe agricultural production issue that results in economic loss. Therefore, early illness detection can provide better outcomes than current detection algorithms. As a result, deep learning approaches based on computer vision might be used to detect diseases early. This stud

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Kanda, Paul Shekonya, Kewen Xia, Anastasiia Kyslytysna, and Eunice Oluwabunmi Owoola. "Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks." Plants 11, no. 21 (2022): 2935. http://dx.doi.org/10.3390/plants11212935.

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based o

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Abqa, Javed Salheen Bakhet Taliah Tajammal Iqra Javed. "Plant Leaf Disease Detection Using CNN." Dialogue Social Science Review (DSSR) 3, no. 2 (2025): 678–94. https://doi.org/10.5281/zenodo.15185006.

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Agriculture is the major area in several regions, including Pakistan and India, where roughly 55% to 60% of the inhabitant relies on it explicitly and implicitly. In agricultural countries, plant disease is a serious issue. Each year farmer faces loss due to the plant diseases and it is a difficult task to detect disease by a naked eye. The suggested approach intends to decrease agricultural losses. An automated plant identification and diagnosis is required for this. The proposed system helps to find and recognize disease at initial phase or at least diagnose the disease to avoid further degr

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Debnath, Anjan, Md Mahedi Hasan, M. Raihan, et al. "A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)." Sensors 23, no. 21 (2023): 8685. http://dx.doi.org/10.3390/s23218685.

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The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf p

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Mohan, Dr K. Madan. "LEAF DISEASE PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–6. http://dx.doi.org/10.55041/ijsrem27703.

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Leaf diseases are a major problem in agriculture, causing significant losses in crop yield and quality. Early detection of leaf diseases is essential for effective management, but it can be difficult and time- consuming to do manually. In recent years, there has been growing interest in the use of machine learning and computer vision techniques for leaf disease prediction. These techniques can be used to automatically extract features from leaf images that are indicative of disease, and then use these features to train a classifier that can distinguish between healthy and diseased leaves. Seve

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Zhang, Keke, Qiufeng Wu, Anwang Liu, and Xiangyan Meng. "Can Deep Learning Identify Tomato Leaf Disease?" Advances in Multimedia 2018 (September 26, 2018): 1–10. http://dx.doi.org/10.1155/2018/6710865.

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This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer

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Okengwu, Ugochi A., Hillard A. Akpughe, Eyinanabo Odogu, and Taiye Ojetunmibi. "Review on Technologies Applied to Classification of Tomato Leaf Virus Diseases." European Journal of Artificial Intelligence and Machine Learning 2, no. 4 (2023): 11–17. http://dx.doi.org/10.24018/ejai.2023.2.4.29.

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Tomato leaf virus diseases present a significant risk to tomato cultivation, leading to substantial financial losses worldwide. Implementing appropriate control measures depends on these diseases being accurately and quickly identified and classified. This article provides an insight into the analysis of the various technologies used to classify tomato leaf virus diseases as well as some similar plant leaf virus disease. The review encompasses both traditional and modern techniques, including image processing, machine learning, and deep learning methods. It explores the use of different imagin

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Anam, Syaiful, Indah Yanti, Zuraidah Fitriah, and M. Hakim Akbar Maulana Assidiq. "Tomato Leaf Disease Segmentation Using Clustering Method Based on FATPSO with Multi Features." International Journal of Emerging Technology and Advanced Engineering 13, no. 1 (2023): 28–36. http://dx.doi.org/10.46338/ijetae0123_04.

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Early blight is one of diseases that infects tomato leaves. This disease causes a decrease in the production of tomato plants. The early detection of this diseases is very important to maintain the tomato production. Monitoring tomato leaves health manually in large area is very time-consuming and inefficient. The drones and computer vision technology give an alternative in solving this problem. One of the important steps in detecting the tomato leaf disease based on computer vision is the segmentation area of the tomato leaf into the healthy and diseased tomato leaf. The K-means clustering of

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D, Femi, Murugasami R, Manikandaprabu N, Raja Paulsingh J, and Vanaja P. "Identification and Classification of Tomato Leaf Diseases Using Machine Learning Techniques." Webology 18, no. 05 (2021): 1168–75. http://dx.doi.org/10.14704/web/v18si05/web18297.

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Tomato is cultivated in all countries of the world in fields, glasshouses etc. China, India, USA, Turkey, Egypt, Iran, Italy, Spain and Brazil are the important countries which are cultivating tomatoes. It is most commonly and widely cultivated in India. India is one of the countries in harvesting tomatoes. Tomato is a vital vegetable yield with respect to both income and food. Tomatoes are for the most part summer crops, yet it tends to improve steadily. Naturally, it contains A and C of vitamins which also acts as an antioxidant to prevent cancerous cells. Since the organic product contains

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Roopa, Ms, and Darshan S. "Detection of Tomato Leaf Infections." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem.spejss006.

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Plant diseases pose a significant threat to agriculture by reducing crop yield and quality, often without early signs noticeable to the naked eye. Among these, tomato crops are especially vulnerable to a variety of diseases that can quickly spread and impact production. This study proposes an effective and automated approach to detecting tomato leaf diseases using deep learning, specifically Convolutional Neural Networks (CNNs). The system is designed to classify tomato leaf images into ten categories: healthy, yellow leaf curl virus (YLCV), bacterial spot (BS), early blight (EB), leaf mold (L

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Sharma, Dibyansu. "Plant Disease Detection Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29945.

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Agriculture plays a crucial role in sustaining societies worldwide, and effective crop management is paramount for food security. Plant diseases pose significant threats to agricultural productivity, making timely detection imperative. Leveraging advancements in deep learning and machine vision, this research explores the application of Convolutional Neural Networks (CNNs) to detect tomato leaf diseases. A novel dataset comprising images of diseased and healthy tomato leaves is introduced and utilized for model training. By employing the Inception V3 architecture and data augmentation techniqu

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NS Wisidagama, FMMT Marikar, and M Sirisuriya. "Detecting tomato leaf diseases with convolutional neural networks and image processing using a Sri Lankan tomato leaf dataset." Ukrainian Journal of Educational Studies and Information Technology 11, no. 4 (2023): 290–301. http://dx.doi.org/10.32919/uesit.2023.04.05.

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Crops like tomatoes are vital to farmers' livelihoods in Sri Lanka, where agriculture is a key economic pillar. But growing tomatoes comes with a lot of difficulties, not the least of which is the possibility of certain diseases that can destroy crops. The timely implementation of interventions and reduction of losses are contingent upon the early discovery of these disorders. Using convolutional neural networks (CNNs) and image processing techniques, this study offers a novel solution to this problem by detecting tomato leaf illnesses. One unique aspect of this study is the use of a custom da

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Saputra, Adi Dwifana, Djarot Hindarto, Ben Rahman, and Handri Santoso. "Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3." SinkrOn 8, no. 2 (2023): 647–56. http://dx.doi.org/10.33395/sinkron.v8i2.12218.

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Tomato diseases vary greatly, one of which is tomato leaf disease. Some variants of leaf diseases include late blight, septoria leaf, yellow leaf curl virus, bacteria, mosaic virus, leaf fungus, two-spotted spider mite, and powdery mildew. By knowing the disease on tomato leaves, you can find medicine for the disease. So that it can increase the production of tomatoes with good quality and a lot of quantity. The problem that often occurs is that farmers cannot determine the disease in plants, they try to find suitable herbal medicines for their plants. After being given the drug, many plants a

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Fanigliulo, A., A. Viggiano, A. Crescenzi, E. Zingariello, R. Liguori, and R. Senn. "CONTROL OF TOMATO YELLOW LEAF CURL DISEASE IN TOMATO." Acta Horticulturae, no. 1069 (February 2015): 191–96. http://dx.doi.org/10.17660/actahortic.2015.1069.27.

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Birhanu, Gardie. "Image-based Tomato Disease Identification Using Convolutional Neural Network." Indian Journal of Science and Technology 14, no. 42 (2021): 3126–32. https://doi.org/10.17485/IJST/v14i42.1164.

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<strong>Objectives:</strong> Agriculture is the main food source and farmers are challenging a great production loss annually due to plant leaf disease. Early identification of tomato plant diseases help farmers to take preventive measure to reduce production loss. As a result, to recognize tomato plant leaf diseases in its early stage, a deep learning approach is discussed.<strong> Methods:</strong> For tomato disease identification and classification a convolutional neural network model is used in this study. CNN is capable for fine-grained disease identification as a techniqu

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., Arshad, Kantharaja S K, Karthik M, Subhash ., and Kiran . "Deep Learning Based Tomato Leaf Disease Detection for Smart Farming." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 05 (2024): 6210–14. http://dx.doi.org/10.15680/ijircce.2024.1205178.

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Tomato cultivation has a major role in global agriculture, contributing to both food security and economic stability. Still, tomato plants are susceptive to various diseases, which can specially reduce yield and quality. Plant disease early detection and treatment depend more and more on automatic disease identification and categorization technologies. In this paper, we present a Deep Learning based approach for tomato leaf diseases classification using VGG16 Convolutional Neural Network architecture. The VGG16 model is well-known for its superior picture categorization ability, A data set is

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Sravan Kumar G, Et al. "An Empirical Performance Analysis of Multi-Classification of Diseases of Tomato Leaf using CNN Models in the Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2090–95. http://dx.doi.org/10.17762/ijritcc.v11i9.9210.

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Tomato farming in India, producing tomatoes is one of the leading productions and stands second-largest producer of tomatoes in the world. Tomato farming has been facing challenges as the crop is susceptible to tomato diseases that include Bacterial_Spot, Early_Blight, Septoria_Leaf Spot, Spider_Mites and Late_Blight, that accounts to massive decline in the crop production. The significant drop in the production raises alarm in the analysis of the leaf of tomato with adoption of state of art technologies into the farming. The analysis of tomato leaf with the intent of early prediction of parti

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S, Mahesh, Nagesh Sugur K S, and Vishwanath Gowda R. "TOMATO LEAF DISEASES PREDICTION." International Research Journal of Computer Science 9, no. 8 (2022): 286–93. http://dx.doi.org/10.26562/irjcs.2022.v0908.26.

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This paper deals with the optimized real time detection of diseases that affect the plant and the area affected using Convolutional Neural Networks (CNN) algorithms. So that appropriate fertilizers can be used to prevent further damage to plants from pathogenic viruses. The activation function is the core of the CNN model as it incorporates the non – linearity to have anauthentic artificial intelligence system for classification. ReLu is one among the best activation functions, but hasa disadvantage that the derivative of the function is zerofor negative values and leads to neuronal necrosis.

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D L, Shanthi, Vinutha K, Ashwini N, and Saurav Vashistha. "Tomato Leaf Disease Detection Using CNN." Procedia Computer Science 235 (2024): 2975–84. http://dx.doi.org/10.1016/j.procs.2024.04.281.

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Glick, E., Y. Levy, and Y. Gafni. "The viral etiology of tomato yellow leaf curl disease – a review." Plant Protection Science 45, No. 3 (2009): 81–97. http://dx.doi.org/10.17221/26/2009-pps.

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Tomato yellow leaf curl disease (TYLCD) is one of the most devastating plant diseases in the world. As a result of its continuing rapid spread, it now afflicts more than 30 tomato growing countries in the Mediterranean basin, southern Asia, Africa, and South, Central and North America. The disease is caused by a group of viral species of the genus <I>Begomovirus,</I> family Geminiviridae (geminiviruses), referred to as <I>Tomato yellow leaf curl virus</I> (TYLCV). These are transmitted by an insect vector, the whitefly<I> Bemisia tabaci</I>, classified in th

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Habibullah, Muhamad, Hisyam Fahmi, and Erna Herawati. "Penerapan Metode Segmentasi Gabor Filter Dan Algoritma Support Vector Machine Untuk Pendeteksian Penyakit Daun Tomat." Jurnal Riset Mahasiswa Matematika 2, no. 6 (2023): 221–32. http://dx.doi.org/10.18860/jrmm.v2i6.22023.

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This research discusses about processing a formulation that we can give to diseased tomato leaves. Gabor Filter is a method used to detect textures using frequency and orientation parameters. The Support Vector Machine (SVM) algorithm is an algorithm that can be used classifying tomato leaf diseases. The purpose of this research is to determine the accuracy of the Gabor Filter segmentation and the Support Vector Machine Algorithm for detecting tomato leaf disease to facilitate farmers in analyzing diseases on tomato leaves. The input will go through pre-processing of RGB pixels to Greyscale on

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Sohel, Amir, Md Mizanur Rahman, Md Umaid Hasan, MD Kafiul Islam, Lamia Rukhsara, and Tapasy Rabeya. "Automated tomato leaf disease recognition using deep convolutional networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1850. https://doi.org/10.11591/ijece.v15i2.pp1850-1860.

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Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This

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Sohel, Amir, Md Mizanur Rahman, Md Umaid Hasan, Md Kafiul Islam, Lamia Rukhsara, and Tapasy Rabeya. "Automated tomato leaf disease recognition using deep convolutional networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1850–60. https://doi.org/10.11591/ijece.v15i2.pp1850-1860.

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Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This

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Arifin, Nurhikma, Maratuttahirah, Juprianus Rusman, and Muhammad Furqan Rasyid. "LEAF DISEASE DETECTION IN TOMATO PLANTS USING XCEPTION MODEL IN CONVOLUTIONAL NEURAL NETWORK METHOD." Jurnal Teknik Informatika (Jutif) 5, no. 2 (2024): 571–77. https://doi.org/10.52436/1.jutif.2024.5.2.1926.

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This study aims to detect leaf diseases in tomato plants by applying the Xception model in the Convolutional Neural Network (CNN) method. The study categorizes tomato conditions into three main categories: Early Blight, Late Blight, and Healthy. Early Blight is generally infected by specific pathogens that cause spots and damage in the early stages of plant growth, while Late Blight is infected by pathogens in the later stages of the growing season. Meanwhile, the healthy category indicates normal conditions without disease symptoms. The dataset used consists of 300 tomato images, with each ca

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Aminu Suleiman, Bashir, Stephen Luka, and Joseph Nda Ndabula. "A CNN-SVM based model for detection and classification of Tomato leaf diseases." Journal of Basics and Applied Sciences Research 3, no. 3 (2025): 8–15. https://doi.org/10.4314/jobasr.v3i3.2.

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Tomato leaf diseases represent a substantial risk to global agriculture, leading to decreased crop yields and inferior fruit quality. Conventional disease detection techniques depend significantly on manual examination, resulting in delays and inaccuracies. This paper investigates the application of machine learning CNN-SVM methodology to create an automated system for the detection and classification of tomato leaf diseases. The research employs datasets from PlantVillage and Labeled_Features, consisting of more than 18,000 images of high-resolution tomato leaves afflicted by diverse diseases

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Wu, Yang, and Lihong Xu. "Image Generation of Tomato Leaf Disease Identification Based on Adversarial-VAE." Agriculture 11, no. 10 (2021): 981. http://dx.doi.org/10.3390/agriculture11100981.

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The deep neural network-based method requires a lot of data for training. Aiming at the problem of a lack of training images in tomato leaf disease identification, an Adversarial-VAE network model for generating images of 10 tomato leaf diseases is proposed, which is used to expand the training set for training an identification model. First, an Adversarial-VAE model is designed to generate tomato leaf disease images. Then, a multi-scale residual learning module is used to replace single-size convolution kernels to enrich extracted features, and a dense connection strategy is integrated into t

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Liu, Wenbo, Chenhao Bai, Wei Tang, Yu Xia, and Jie Kang. "A Lightweight Real-Time Recognition Algorithm for Tomato Leaf Disease Based on Improved YOLOv8." Agronomy 14, no. 9 (2024): 2069. http://dx.doi.org/10.3390/agronomy14092069.

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To address the real-time detection challenge of deploying deep learning-based tomato leaf disease detection algorithms on embedded devices, an improved tomato leaf disease detection algorithm based on YOLOv8n is proposed in this paper. It is able to achieve the efficient, real-time detection of tomato leaf diseases while maintaining model’s lightweight requirements. The algorithm incorporated the LMSM (lightweight multi-scale module) and ALSA (Attention Lightweight Subsampling Module) to improve the ability to extract lightweight and multi-scale semantic information for the specific characteri

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Ibrahim, Shafaf, Nur Afiqah Mohd Fuad, Nor Azura Md Ghani, Raihah Aminuddin, and Budi Sunarko. "Support Vector Machine (SVM) for Tomato Leaf Disease Detection." AGRIVITA Journal of Agricultural Science 47, no. 2 (2025): 338. https://doi.org/10.17503/agrivita.v47i2.3746.

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<p>Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment te

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Rubul, Kumar Bania. "Hybrid ResNet50-PCA based deep transfer learning approach for classification of tomato leaf diseases." Journal of Biodiversity and Environmental Sciences (JBES) 22, no. 4 (2023): 42–48. https://doi.org/10.5281/zenodo.10279676.

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Tomato is one of the world's most indispensable and consumable vegetable items. In the Indian market, it has high commercial value, and it is produced in huge quantities. The crop sensitivity and climatic conditions have made diseases familiar in the tomato crop during all the stages of its growth. It is a difficult task to monitor plant diseases manually due to its complex nature and time-consuming process. Artificial intelligence (AI) based computational models can detect leaf diseases in their early stages. In this article, ResNet50 a deep transfer learning based Convolutional Neural Networ

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Kusuma, Adji Putra Nugraha, Richard Anthony Lim, Nicholas Ananda Khosasi, et al. "Comparative Analysis of Machine Learning Models in Detecting Tomato Leaf Diseases." IOP Conference Series: Earth and Environmental Science 1488, no. 1 (2025): 012099. https://doi.org/10.1088/1755-1315/1488/1/012099.

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Abstract This paper attempts to estimate five classes: four different disease classes of a tomato plant leaf and one class of a healthy tomato plant leaf. The models tested include logistic regression, support vector machine (SVM), and neural network, all integrated into a voting classifier. The data is taken from an online dataset of plant diseases and tested with random tomato plant leaf images from the internet. By using Python, scikit-learn, and Keras libraries, when comparing the accuracy of the different models, the neural network achieved the highest accuracy at 78%, followed by the sup

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Sundararaman, Bharathwaaj, Siddhant Jagdev, and Narendra Khatri. "Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review." Sustainability 15, no. 15 (2023): 11681. http://dx.doi.org/10.3390/su151511681.

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The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields in the face of numerous challenges, including plant diseases. Tomato is a widely cultivated and essential food crop that is particularly susceptible to disease, resulting in significant economic losses and hindrances to food security. Recently, Artificial Intelligence (AI) has emerged as a promising tool for detecting and classifying tomato leaf diseases with exceptional accuracy and efficiency, empowering farmers to take proactive measures to prevent crop damage and

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Silva, Laércio J. da, Carla do C. Milagres, Derly José H. da Silva, Carlos Nick, and João Paulo A. de Castro. "Basal defoliation and their influence in agronomic and phytopathological traits in tomato plants." Horticultura Brasileira 29, no. 3 (2011): 377–81. http://dx.doi.org/10.1590/s0102-05362011000300020.

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The incidence of leaf diseases is one of the main factors limiting the tomato crop production, increasing the production cost due to excessive pesticide application. The basal leaf removal could reduce inoculum sources, disease severity and contribute to reducing the use of pesticide. Aiming to evaluate the efficiency of this practice on the reduction of tomato leaf diseases and the effect in the quality and in the productivity of the tomato plants for in natura consumption, two experiments were carried out to test four levels of basal leaf removal. Basal leaves removal, at fruit harvesting, i

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Khan, A. J., S. Akhtar, A. K. Singh, and R. W. Briddon. "A Distinct Strain of Tomato leaf curl Sudan virus Causes Tomato Leaf Curl Disease in Oman." Plant Disease 97, no. 11 (2013): 1396–402. http://dx.doi.org/10.1094/pdis-02-13-0210-re.

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Tomato leaf curl disease (ToLCD) is a significant constraint for tomato production in the Sultanate of Oman. The disease in the north of the country has previously been shown to be caused by the monopartite begomoviruses (family Geminiviridae) Tomato yellow leaf curl virus and Tomato leaf curl Oman virus. Many tomato plants infected with these two viruses were also found to harbor a symptom enhancing betasatellite. Here an analysis of a virus isolated from tomato exhibiting ToLCD symptoms originating from south and central Oman is reported. Three clones of a monopartite begomovirus were obtain

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Lestari, Cynthia Ayu Dwi, Syaiful Anam, and Umu Sa’adah. "Exploring DenseNet architectures with particle swarm optimization: efficient tomato leaf disease detection." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1377. https://doi.org/10.11591/ijai.v14.i2.pp1377-1385.

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The critical challenge of tomato leaf disease demands effective solutions surpassing manual detection limitations, ensuring rapid intervention, optimal crop health, and maximizing yield for farmers. DenseNet, a convolutional neural network (CNN) architecture, is lauded for its adept handling of gradient flow issues by extensive interlayer connectivity. Its application holds significant promise in tackling the intricate task of identifying tomato leaf diseases. This research introduces an innovative methodology employing particle swarm optimization (PSO) to fine-tune the DenseNet architecture a

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Cynthia, Ayu Dwi Lestari, Anam Syaiful, and Sa'adah Umu. "Exploring DenseNet architectures with particle swarm optimization: efficient tomato leaf disease detection." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1377–85. https://doi.org/10.11591/ijai.v14.i2.pp1377-1385.

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The critical challenge of tomato leaf disease demands effective solutions surpassing manual detection limitations, ensuring rapid intervention, optimal crop health, and maximizing yield for farmers. DenseNet, a convolutional neural network (CNN) architecture, is lauded for its adept handling of gradient flow issues by extensive interlayer connectivity. Its application holds significant promise in tackling the intricate task of identifying tomato leaf diseases. This research introduces an innovative methodology employing particle swarm optimization (PSO) to fine-tune the DenseNet architecture a

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Gatla, Anitha, S. R. V. Prasad Reddy, Deenababu Mandru, et al. "Optimizing Edge AI for Tomato Leaf Disease Identification." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 16061–68. http://dx.doi.org/10.48084/etasr.7802.

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This study addresses the critical challenge of real-time identification of tomato leaf diseases using edge computing. Traditional plant disease detection methods rely on centralized cloud-based solutions that suffer from latency issues and require substantial bandwidth, making them less viable for real-time applications in remote or bandwidth-constrained environments. In response to these limitations, this study proposes an on-the-edge processing framework employing Convolutional Neural Networks (CNNs) to identify tomato diseases. This approach brings computation closer to the data source, red

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