[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124061-en":3,"doc-seo-124061-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124061,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Encouraging hygiene permanence in tomato leaf and applying machine learning techniques","Tomato leaf disease reduces crop growth and food production through infectious agents and environmental stress, producing symptoms such as leaf spots, discoloration, deterioration, yellowing, and softening. The study consolidates prior research on detection and prevention while focusing on hygiene permanence for leafy vegetables. A convolutional neural network with a pre-trained model is applied to a tomato leaf image dataset, supporting early-stage disease prediction to improve agricultural decision-making and guide future machine-learning contributions.","Encouraging hygiene permanence in tomato leaf and applying  \nmachine learning techniques  \nSaravanan Madderi Sivalingam1, Lakshmi Devi Badabagni2  \n1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and  \nTechnical Sciences, Chennai, India  \n2Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India  \nArticle history:  \nReceived Sep 21, 2023 Revised Nov 1, 2023 Accepted Nov 7, 2023  \nKeywords:  \nAgriculture Disease  \nFood production Hygiene  \nLeafy vegetable Machine learning Tomato  \nCorresponding Author:  \nTomatoes 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, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.  \nThis is an open access article under the CC BY-SA license.  \nSaravanan Madderi Sivalingam  \nDepartment of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences  \nThandalam, Chennai, India  \nEmail: [saranenadu@gmail.com](saranenadu@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nPlant growth depends on factors like water, light, temperature, nutrients and plant hormones. A drastic change in these factors leads to plant diseases. However, these are natural factors that cause diseases. But plant diseases are also majorly caused by the pathogenic organisms such as fungi, bacteria, viruses, protozoa, insects and parasitic plants which are termed as infectious plant diseases. These diseases may affect various parts of the plant which include leaf, root, and stem [1] . When leaves get affected by pests or pathogens or environmental stress, they end up getting affected. These effects include brown spots, white spots, holes, leaf discoloration, leaf deterioration, leaf softening, yellowing of leaves and sticky leaves. Nowadays, the majority of plants are affected by leaf spots and other leaf diseases. These can be prevented by proper crop management techniques [2]. Various disease resistant plants are being produced and cultivated by biotechnological research techniques to encourage hygiene permanence in leafy vegetable plants [3] .  \nTomato cultivation is done with three types of methods, they are Beta-carotene, vitamin C and vitamin E. When the disease affects tomato leaf, it mostly affects the leaf and stem. India is third in position for tomato production among various counties in the world, in India forty thousand hectares of tomato planted  \nfor every year [4] . Therefore, India needs emergency precaution to increase the production rate of tomato by reducing the disease on tomato leaf [5] . Therefore, research gap identified from various research studies are used to encourage the hygiene permanence on vegetable leaves, for this convolutional neural network (CNN) machine learning algorithm applied on toma","cbCaiu0o4DDArDKe","https://ap.wps.com/l/cbCaiu0o4DDArDKe","pdf",421212,1,7,"English","en",105,"# Article Info\n## Abstract\n## Introduction\n## Research Method","[{\"question\":\"What is the main problem addressed in this research?\",\"answer\":\"The research focuses on encouraging hygiene permanence in tomato leaves by detecting diseases early, so crop production and quality losses can be reduced.\"},{\"question\":\"How is machine learning used to predict tomato leaf disease?\",\"answer\":\"A convolutional neural network (CNN) is applied to pre-processed tomato leaf images, using a pre-trained model such as ResNet50 to predict disease at an early stage.\"},{\"question\":\"What dataset size and preprocessing steps are mentioned?\",\"answer\":\"The research method describes using 8,567 tomato leaf images, followed by preprocessing before training and prediction with the CNN approach.\"}]","Encouraging hygiene permanence in tomato leaf and applying machine learning techniques | 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