PREDICTIVE CULTIVATION: INTEGRATING METEOROLOGICAL DATA AND MACHINE LEARNING FOR ENHANCED CROP YIELD FORECAST

被引：0

作者：

Kalyani, B. J. D. ^{[1
]}

Shahanaz, Shaik ^{[2
]}

Sai, Kopparthi Praneeth ^{[3
]}

机构：

[1] Inst Aeronaut Engn, Dept Comp Sci & Engn, Hyderabad, Telangana, India

[2] Vardaman Coll Engn, Dept Comp Sci & Engn, Hyderabad, India

[3] Lamer Univ, Dept Comp Sci, Beaumont, TX USA

来源：

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE | 2024年 / 25卷 / 06期

关键词：

Prediction Cultivation; Machine Learning; Smart Agriculture; Random Forest; Meteorological data;

D O I：

10.12694/scpe.v25i6.3304

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Agriculture is a key component of Telangana's economy, and greater performance in this sector is crucial for inclusive growth. A central challenge is yielding estimation to predict crop yields before harvesting. This paper addresses this challenge with machine learning approaches includes Naive Bayes, KNN and Random Forest. The parameters considered for model testing are crop, season, rainfall and location. This paper includes a case study of Telangana with the help of Telangana weather data set to provide analysis on the key factors like overall rainfall recorded with respect to each Mandal, overall seasonal yield in selected years, seasonal yield of major crops like Bengal gram, groundnut and maize, and overall yield in two different agricultural seasons: rabi and kharif. Random forest machine learning model produces highest accuracy of 99.32% when compared with other process models.

引用

页码：4661 / 4668

页数：8

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