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Crop and Fertilizer Prediction and Disease Detection Using Data Science
Subject area: Science,Engineering and Technology · Area of research: Data Science
Abstract
One of the key industries that affect a nation's economic development is agriculture. The bulk of people in countries like India relies on agriculture for their livelihood. For growth and increased output, plants require nutrients. Plants can get the nutrients they need from fertilizers, manure, and soil. By putting the plant under both abiotic and biotic stress, climate change and global warming were found to be the primary causes of crop loss. One of the main areas of focus for academics globally is figuring out the causes of and solutions to the issues associated with crop loss [1], it is not only causing crop loss but is now affecting food production and crop prediction, which will have a detrimental effect on farmers' economies by lowering yields and making them less skilled at predicting future harvests. This research project helps inexperienced farmers (Tech farmers) plant the right crops by utilizing Data Science, one of the most advanced technologies in mining and forecasting. To produce more agricultural products with less waste, the agricultural sector needs technological advancements. Therefore, our major goal is to make it simple for farmers or other users to work on their farms by developing a website that includes crop and fertilizer forecasting as well as plant disease detection. Overall, the integration of crop prediction, fertilizer prediction, and disease detection using Data science can improve agricultural sustainability, productivity, and profitability. By leveraging these technologies, farmers can reduce crop loss due to disease outbreaks, increase crop yield and quality, and minimize the negative impact of agriculture on the environment.
Keywords
Decision Trees, Flask, Forecasting, Random Forests, Residual Network, Support Vector Machine, Web Technologies.
References
[1] S. Roy, R. Ray, S. R. Dash, and M. K. Giri, 2021. Plant disease identification with a focus on dimensionality reduction using machine learning technologies. A Machine Learning Perspective on Data Analytics in Bioinformatics, pp. 109–144.
[2] R. Balodi, S. U. N. I. N. A. Bisht, A. Ghatak, and K. H. Rao, 2017. Diagnostic problems and technical improvements for plant diseases. Pages. 275-281 in Indian Phytopathology, 70(3).
[3] Agro algorithm in Hadoop for crop production prediction, Kushwaha, A.K. and Bhattacharya, S., 2015. IJCSITS, 5(2), pp. 271-274. International Journal of Computer Science and Information Technology & Security.
[4] Detecting plant diseases using image processing approaches, Kulkarni, A.H. and Patil, 2012. pp. 3661–3664 in International Journal of Modern Engineering Research, 2(5).
[5] K. Neha, V. Spandana, V. S. Vaishnavi, Y. Jeevan Nagendra, and V. G. R. R. Devi. supervised machine learning approach for predicting crop yield in the agricultural sector Pages 736-741 of the 2020 Fifth International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020.
[6] Bhagyashri Dadore, D. Anantha, Reddy, and Aarti Watekar. "A machine learning-based crop recommendation system to enhance agricultural production in rate area." 6(1) (2019), 485-489, in International Journal of Scientific Research in Science and Technology.
[7] Avinash Devare, Mitalee Pendke, Rohit Kumar, Ankit Pawar, Pooja Shinde, and Rajak. "A crop recommendation system employing machine learning to enhance agricultural productivity." 4, no. 12 (2017): 950-953 for the International Research Journal of Engineering and Technology.
How to cite this paper
@article{1704150,
author = {M Goutham, Konaganti Pravalya, G. Naga Vamsi, Royyuru Srikanth},
title = {Crop and Fertilizer Prediction and Disease Detection Using Data Science},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {9},
pages = {67-72},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704150.pdf},
abstract = {One of the key industries that affect a nation's economic development is agriculture. The bulk of people in countries like India relies on agriculture for their livelihood. For growth and increased output, plants require nutrients. Plants can get the nutrients they need from fertilizers, manure, and soil. By putting the plant under both abiotic and biotic stress, climate change and global warming were found to be the primary causes of crop loss. One of the main areas of focus for academics globally is figuring out the causes of and solutions to the issues associated with crop loss [1], it is not only causing crop loss but is now affecting food production and crop prediction, which will have a detrimental effect on farmers' economies by lowering yields and making them less skilled at predicting future harvests. This research project helps inexperienced farmers (Tech farmers) plant the right crops by utilizing Data Science, one of the most advanced technologies in mining and forecasting. To produce more agricultural products with less waste, the agricultural sector needs technological advancements. Therefore, our major goal is to make it simple for farmers or other users to work on their farms by developing a website that includes crop and fertilizer forecasting as well as plant disease detection. Overall, the integration of crop prediction, fertilizer prediction, and disease detection using Data science can improve agricultural sustainability, productivity, and profitability. By leveraging these technologies, farmers can reduce crop loss due to disease outbreaks, increase crop yield and quality, and minimize the negative impact of agriculture on the environment.},
keywords = {Decision Trees, Flask, Forecasting, Random Forests, Residual Network, Support Vector Machine, Web Technologies.},
month = {March},
}