Crop Prediction for Smart Agriculture Using Ensemble of Classifiers

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Author(s): Khushal Kindra (Vellore Institute of Technology, Chennai, India)and Bhuvaneswari Amma N. G. (Vellore Institute of Technology, Chennai, India)
Copyright: 2023
Pages: 18
Source title: Machine Learning and Deep Learning for Smart Agriculture and Applications
Source Author(s)/Editor(s): Mohamamd Farukh Hashmi (National Institute of Technology, Warangal, India)and Avinash G. Kesakr (Visvesvaraya National Institute of Technology, India)
DOI: 10.4018/978-1-6684-9975-7.ch007

Keywords: Agricultural and Food Sciences / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Nowadays due to the advancement in technology, smart agriculture is in the evolving stage. Agricultural farmers worldwide commonly utilize the process of cultivating and harvesting crops to produce food and fiber. Therefore, crop prediction is vital for smart agriculture and the proposed approach involves utilizing all the necessary resources to facilitate crop growth and maintenance. Crop cultivation used to be carried out based on farmers' actual experience. For farmers and agricultural decision-makers to make prompt and accurate judgments that will impact the caliber of agricultural harvests, advanced tools are required. A prediction has been supplanted by machine learning techniques as a result. To anticipate agricultural yield, the authors provide a prediction system based on an ensemble of machine learning classifiers. Also, they discovered that an ensemble technique provided more accurate prediction than using the currently available categorization algorithms.

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