[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121756-en":3,"doc-seo-121756-105":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},121756,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting","Research focuses on predicting suitability of soil for cultivating high-quality grapes while supporting sustainable farming goals such as reducing pesticide use, conserving water, and maintaining soil health. Soil and water characteristics from 154 villages in Nashik (the “Grape Capital of India”) were collected and tested in a government laboratory, then combined with climatic variables, petiole traits, and fruit characteristics to build a dataset. Six machine learning algorithms classified whether soil is fit for grapes, and nutrient correlations were analyzed with equal emphasis on micro and macro nutrients. Results indicate Pimpalas Ramche provides higher nutrient levels and a decision tree classifier achieves strong accuracy.","Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting  \nSwati Vishal Sinha1*, B.M. Patil1  \n1Department of School of Computer Engineering & Technology,  \nDr. Vishwanath Karad, MIT World Peace university, Pune, India  \nMIT-WPC, S.No. 124, Paud Road, Kothrud  \n* Corresponding author mail: [1032201475@mitwpu.edu.in](1032201475@mitwpu.edu.in)  \nAbstract----Minimizing pesticide use, preserving water, as well as enhancing soil health are just a few of the sustainable farming techniques that must be carefully considered while growing grapes of a high calibre. These practices can help preserve the environment and ensure the longevity of the vineyard. However, it is difficult for the farmers to find the suitability of the soil and its environment to cultivate grapes with high quality. Thus this research aims to evaluate the fitness of the soil for the fitness of growing quality grapes with the aid of machine learning algorithm. The research was done on Nasik region which is called as the “Grape Capital of India” situated in Maharashtra. Total of 154 villages were considered for the examination and soil specimens were collected and sent to the government testing lab in Maharashtra. The soil characteristics by considering both micro and macro nutrients, and the water characteristics were obtained from the lab. Also the climatic features, quality of the petiole and fruit characteristics were included for creating the dataset. These data was given to six different machine learning algorithm to classify the soil by defining whether the soil is fit for grapes or not. Moreover, this research proposed to analyze the correlation between the nutrients by which the relationship and dependency between the different nutrients and features were considered for defining the grapes quality. Also both the micro and macro nutrients were given equal importance in defining the soil quality suitable for obtaining high quality grapes. Based on the results obtained, Pimpalas Ramche contains more nutrients for the grape to grow more successfully based on samples gathered from different vine yards and the decision tree classifier scores better than any other classifiers among the machine learning algorithms employed in terms of accuracy.  \nKeywords-Nitrogen, Machine Learning, Vineyard, Random forest, Decision tree, Nave Bayes, Support Vector Machine.  \nI. INTRODUCTION  \nA major portion of the population is employed in agriculture, which is a key sector of the Indian economy and contributes substantially to the GDP of the nation. India is second in the globe for the production of agriculture, which includes grains, fruits, vegetables, and spices. Grape cultivation is an important horticultural activity in India, with the country being one of the world's largest producers of grapes [1] . The majority of grape cultivation in India is focused on table grapes, which are used for direct consumption, although some grapes are also used for wine production. Due to its importance to the Indian economy & addition to export revenue, grape farming is a significant sector of agriculture. The eighth-largest producer of grapes in the world is India, and the production of grapes provides a significant portion of the country's farmers with a living [2] .  \nIndia is home to several grape-growing regions, primarily located in the southern and western parts of the country. Nashik, located in the western part of Maharashtra, has a mild climate and produces high-quality table grapes. Sangli, another major grape-growing region in Maharashtra, has a warm and dry climate and produces high-quality grapes. Bijapur, located in the northern part of Karnataka, is another region that is ideal for growing grapes due to its semi-arid climate [3] . Hyderabad, located in Telangana, is known for its high-quality grapes grown  \nin the surrounding areas. In Tamil Nadu, the districts of Coimbatore, Krishnagiri, and Dharmapuri are known for their high-quality table grapes grow","cbCait7ajzbHSje7","https://ap.wps.com/l/cbCait7ajzbHSje7","pdf",589569,1,11,"English","en",105,"# Introduction\n## Background and importance of grape cultivation\n## Key factors affecting grape quality\n## Role of soil properties in grape growth","[{\"question\":\"What is the main goal of the research on grapes?\",\"answer\":\"To evaluate whether soil is suitable for growing high-quality grapes by using machine learning based on pre-harvesting soil, water, climate, and plant/fruit characteristics.\"},{\"question\":\"How was the dataset created in the study?\",\"answer\":\"Soil specimens from 154 villages in the Nashik region were tested for micro and macro nutrients and water characteristics, and the dataset also included climatic features, petiole quality, and fruit characteristics.\"},{\"question\":\"Which machine learning model performed best according to the results?\",\"answer\":\"The decision tree classifier achieved better accuracy than the other classifiers among the six machine learning algorithms used.\"}]","Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting | 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is the main goal of the research on grapes?","Question",{"text":75,"@type":76},"To evaluate whether soil is suitable for growing high-quality grapes by using machine learning based on pre-harvesting soil, water, climate, and plant/fruit characteristics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset created in the study?",{"text":80,"@type":76},"Soil specimens from 154 villages in the Nashik region were tested for micro and macro nutrients and water characteristics, and the dataset also included climatic features, petiole quality, and fruit characteristics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best according to the results?",{"text":84,"@type":76},"The decision tree classifier achieved better accuracy than the other classifiers among the six machine learning algorithms 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