Home / Current Issue / Paper 1703435
Study On: Soil Based Crop Prediction and Weather Forecasting
Subject area: Science,Engineering and Technology · Area of research: Machine learning
Abstract
In Indian economy, agriculture contributes 18% of total India GDP. A model is proposed for predicting soil series and providing suitable crop yield suggestion for that specific soil and weather. The model has been tested by applying different Deep algorithm. CNN shows highest accuracy in soil classification and suggests crops with less time. The type of soil is clay, peat, sand, humus clay. It gives us more accuracy as compared to existing system and gives more benefit to farmers. Crop prediction helps us for increasing crop production. In this paper, a low cost result given for crop.
Keywords
Crop Prediction, CNN Algorithm, python.
References
[1] “Machine learning methods for crop yield prediction and climate change impact assessment in agriculture” by authors Andrew Crane Droesch, Published in IOP Ltd Volume:05 | OCT-2018.
[2] “Automated weather event analysis with machine learning”, by authors N. Hasan, M.T. Uddin, and N.K.Chowdhury, Published in IEEE 2016 International Conference on Innovations in science, Engineering and Technology (ICISET),2016,pp.1-5.
[3] “Weather forecasting using deep learning techniques” by authors A.G. Salman, B. Kanigoro, and Y. Heryadi, Published in IEEE 2015 International Conference on Innovations in science, Engineering and Technology (ICISET),2015, pp.281-285.
[4] “Internet of Things (IOT) for precision Agriculture Application” by authors Manishkumar Dholu, Mrs. K.A. Ghodinde, Published in 2nd International Conference on Trends in Electronics and Informations (ICOEI 2018) IEEE Conference Record: #42666; IEEE Xplore ISBN:978-15386-3570-4.
[5] “Secure smart agriculture monitoring technique through isolation”, by authors George Suciu, Cristiana-Ioana Istrate and Maria Cristina Ditu, Published Global IOT Summit (GIoTS),2019.
[6] “Object Based and Pixel based Classification Using Rapideye Satellite Imager of ETI_OSA, Lagos, Nigeria” by authors E. Makinde, A. Salami, J. Olaleya and O. Okewusi, Published in Geoinformatics FCE CTU, vol. 15, no. 2, p. 59,2016. Available 10.14311/gi.15.2.5.
[7] “Rainfall prediction using Machine Learning Techniques” by authors Aakash Parmar & Mithila Sompura.
[8] “Weather forecasting using machine learning” by authors Nithin Singh & saurabh chaturvedi.
[9] www.techcentral.ie
[10] www.geeksforgeeks.org
[11] www.win.tue.nl
[12] www.dataversity.net
How to cite this paper
@article{1703435,
author = {Vishakha Kolhe, Shivani Andure, Harshada Beldar, Rutuja Gund},
title = {Study On: Soil Based Crop Prediction and Weather Forecasting},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {11},
pages = {101-104},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703435.pdf},
abstract = {In Indian economy, agriculture contributes 18% of total India GDP. A model is proposed for predicting soil series and providing suitable crop yield suggestion for that specific soil and weather. The model has been tested by applying different Deep algorithm. CNN shows highest accuracy in soil classification and suggests crops with less time. The type of soil is clay, peat, sand, humus clay. It gives us more accuracy as compared to existing system and gives more benefit to farmers. Crop prediction helps us for increasing crop production. In this paper, a low cost result given for crop.},
keywords = {Crop Prediction, CNN Algorithm, python.},
month = {May},
}