Voting Classifier-Based Crop Recommendation

• Published in: SN computer science Vol. 4; no. 5; p. 516
• Main Authors: Bandi, Raswitha, Likhit, M. Sai Surya, Reddy, S. Rajavardhan, Bodla, Sathwik Raj, Venkat, Vempati Sai
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2023; Springer Nature B.V
• Subjects: Agricultural production; Agriculture; Algorithms; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Crop production; Crop yield; Data Structures and Information Theory; Datasets; Decision trees; Enabling Innovative Computational Intelligence Technologies for IOT; Farmers; Farming; Information Systems and Communication Service; Literature reviews; Machine learning; Pattern Recognition and Graphics; Population growth; Rain; Recommender systems; Regression analysis; Review Article; Software Engineering/Programming and Operating Systems; Soil properties; Soils; Suicides & suicide attempts; Vision; Young adults; India; Voting classifier; Ensemble model; Crop recommendation; Machine learning; Recommendation system
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-023-01995-8

The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent.