Crop Prediction Using Data Mining and Machine Learning Techniques

Vanarase, Vaibhavi; Mane, Vrushali; Bhute, Harsha; Tate, Ankita; Dhar, Simran

doi:10.1109/icirca51532.2021.9544724

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Numerous researchers have leveraged machine learning (ML) techniques for crop selection prediction, as evidenced by a range of studies [12][13][14][15]. In addition, several papers have delved into crop yield estimation through the utilization of ML algorithms [16][17][18][19][20], aiming to enhance accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Kakade,

Kulkarni,

Dhawale

et al. 2023

Green Intell. Syst. Appl.

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Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

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