Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

查看全文 作　　者：SVinson [1]Joshua;ASelwin Mich [1]Priyadharson;Raju [2]Kannadasan;Arfat Ahmad [3]Khan;Worawat [3]Lawanont;Faizan Ahmed [4]Khan;Ateeq Ur [5]Rehman;Muhammad Junaid [6]Ali

机构地区：[1]Department of Electronics and Communication Engineering,Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology,Chennai,600062,India;[2]Department of Electrical and Electronics Engineering,Sri Venkateswara College of Engineering,Sriperumbudur,602117,India;[3]Suranaree University of Technology,Nakhon Ratchasima,30000,Thailand;[4]University of Central Punjab,Lahore,54000,Pakistan;[5]Government College University,Lahore,54000,Pakistan;[6]Virtual University of Pakistan,Islamabad Campus,45550,Pakistan

出　　处：《Computers, Materials & Continua》索引2022年第9期,共17页

摘　　要：The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields. 关键词：Machine learning crop yield PREDICTION computer simulation and modelling

相关文献

参考文献（28）
引证文献（3）
耦合文献（4）

参考文献(28)

引证文献(3)

耦合文献(4)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

参考文献(28)

引证文献(3)

耦合文献(4)

用户提示