Enhancing Crop Productivity Through Land Dataset Analysis on Selecting the Right Crops for the Right Land

Author:

Kamatchi C. Bala1,Muthukumaravel A.1

Affiliation:

1. Bharath Institute of Higher Education and Research, India

Abstract

This chapter examines the enhancement of agricultural production utilizing sophisticated data analysis methods, specifically analyzing land datasets and mapping crop suitability. Using geospatial data integration and spatial analytic approaches facilitates the assessment of land suitability for different crops. This enables agricultural stakeholders to make educated decisions. The suitability map findings specify regions with different degrees of appropriateness, guiding crop choices and land use planning. Furthermore, comparing crop production data across numerous years uncovers trends and patterns in agricultural performance. The presented data illustrates variances in crop production, financial gain, and ecological consequences, providing valuable observations on changes throughout time and pinpointing opportunities for improvement. The significance of data-driven methodologies in advancing sustainable agriculture is emphasized in our research, emphasizing the need for well-informed decision-making and focused interventions to improve agricultural output while mitigating environmental consequences.

Publisher

IGI Global

Reference29 articles.

1. Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey

2. A comparative survey of deep learning-based techniques and tools used in modern farming;A.Carmine;Lecture Notes in Networks and Systems,2022

3. Classification of crop based on macronutrients and weather data using machine learning techniques

4. An effective crop recomendation method using machine learning techniques.;D.Garg;International Journal of Advanced Technology and Engineering Exploration,2023

5. Crop Recommendation System using Machine Learning

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3