A Review on Soil Classification using Machine Learning and Crop Suggestions

Author:

Dr. Sulochana Sonkamble 1,Punit Jadhav 2,Vaishnavi Sanjay Jadhav` 2,Akanksha Kavitake 2,Rohan Kolhi 2

Affiliation:

1. HOD, Associate Professor, Department of Computer Engineering, JSPM Narhe Technical Campus, Pune, Maharashtra, India

2. Department of Computer Engineering, JSPM Narhe Technical Campus, Pune, Maharashtra, India

Abstract

India is a primarily agricultural nation. Agriculture is currently the most significant emerging sector in the actual world and the key industry and economic pillar of our nation. The area of agricultural information technology has recently undergone significant changes that have made crop yield prediction an interesting research topic. Crop yield prediction is a technique for estimating crop yield using many characteristics, including temperature, rainfall, fertilizer, insecticides, and other climatic variables and parameters. The use of data mining tools is very common in agriculture. Agriculture uses data mining tools to forecast agricultural production for upcoming years and evaluates these techniques. This system provides an overview of the investigation of agricultural yield prediction using Support Vector Machines(SVM) and K-Nearest Neighbors (KNN).

Publisher

Technoscience Academy

Subject

General Medicine

Reference14 articles.

1. Mamunur Rashid , Yusri Yusup, “A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction”, IEEE , 2021

2. Jaydeep Yadav and Shalu Chopra, “Soil Analysis and Crop Fertility Prediction using Machine Learning”, International Journal of Innovative Research in Advanced Engineering, 2021 3

3. M.Kishore,” Crop Prediction using Machine Learning”, IEEE 2020.

4. Mythresh A and Lavanya B, “Crop Prediction using Machine Learning”, International Research Journal of Engineering and Technology , 2020

5. Arun Kumar, Naveen Kumar, Vishal Vats.”Efficient crop yield prediction using machine learning algorithms”, IJRET Volume: 05 Issue: 06, June-2018, pp 3151-3159

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