Optimization of Crop Recommendations Using Novel Machine Learning Techniques

Husam Lahza, K. R. Naveen Kumar, B. R. Sreenivasa (), Tawfeeq Shawly, Ahmed A. Alsheikhy, Arun Kumar Hiremath and Hassan Fareed M. Lahza
Additional contact information
Husam Lahza: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 23589, Saudi Arabia
K. R. Naveen Kumar: Department of Computer Science & Engineering, Bapuji Institute of Engineering & Technology, Davangere 577004, Karnataka, India
B. R. Sreenivasa: Department of Information Science & Engineering, Bapuji Institute of Engineering & Technology, Davangere 577004, Karnataka, India
Tawfeeq Shawly: Department of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 23589, Saudi Arabia
Ahmed A. Alsheikhy: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
Arun Kumar Hiremath: Department of Computer Science & Engineering, Bapuji Institute of Engineering & Technology, Davangere 577004, Karnataka, India
Hassan Fareed M. Lahza: Department of Information Systems, College of Computers and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia

Sustainability, 2023, vol. 15, issue 11, 1-18

Abstract: A farmer can use machine learning to make decisions about what crops to sow, how to care for those crops throughout the growing season, and how to predict crop yields. According to the World Health Organization, agriculture is essential to the nation’s quick economic development. Food security, access, and adoption are the three cornerstones of the organization. Without a doubt, the main priority is to ensure that there is enough food for everyone. Increasing agricultural yield can help ensure a sufficient supply. The country-wide variation in crop yields is substantial. As a result, this will be the foundation for research into whether cluster analysis can be used to identify crop yield patterns in a field. Previous study investigations were only marginally successful in accomplishing their primary intended objectives because of unstable conditions and imprecise methodology. The vast majority of farmers base their predictions of crop yield on prior observations of crop growth in their farms, which can be deceptive. Standard preprocessing methods and random cluster value selection are not always reliable, according to the literature. The proposed study overcomes the shortcomings of conventional methodology by highlighting the significance of machine learning-based classification/partitioning and hierarchical approaches in offering a trained analysis of yield prediction in the state of Karnataka. The dataset used for the study was collected from the ICAR-Taralabalu Krishi Vigyan Kendra, Davangere, Karnataka. In the two dataset analysis techniques employed in the study to find anomalies, crop area, and crop production are significant variables. Crop area and crop yield are important variables in the two dataset analysis methods used in the study to detect anomalies. The study emphasizes the importance of a mathematical model and algorithm for identifying yield trends, which can assist farmers in selecting crops that have a large seasonal impact on yield productivity.

Keywords: hierarchical clustering; precision agriculture; yield prediction; cluster analysis; dendrogram; partition clustering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/11/8836/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/11/8836/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:11:p:8836-:d:1159881

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().