Soil moisture content prediction model for tea plantations based on SVM optimised by the bald eagle search algorithm. Issue 4 (4th September 2021)

• Record Type: Journal Article
• Authors: Huang, Ying; Jiang, Hao; Wang, Wen‐feng; Wang, Weixing; Sun, Daozong
• Is Part Of: Cognitive computation and systems. Volume 3:Issue 4(2021)
• Page Start: 351
• Page End: 360
• Publication Date: 2021-09-04
• Subjects: bald eagle search algorithm -- precision irrigation -- soil moisture content -- support vector machine; irrigation -- particle swarm optimisation -- mean square error methods -- support vector machines -- genetic algorithms -- soil -- search problems -- crops; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12034
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: In order to solve the problem of low accuracy and efficiency of soil moisture content prediction in tea plantations and improve the level of soil water content prediction, a soil moisture content prediction model for tea plantations based on the support vector machine (SVM)‐optimised bald eagle search (BES) algorithm (BES‐SVM) is proposed. Soil data and environmental data of tea plantations were transmitted to the server using sensor nodes and weather station nodes. The prediction models of soil moisture content and natural environmental parameters such as soil electrical conductivity, soil temperature, air temperature, air humidity, light intensity, and rainfall were developed using the SVM model optimised by the bald eagle search algorithm, and the mean square error (MSE) and coefficient of determination ( R 2 ) were calculated to evaluate the model performance. Meanwhile, the performance of the BES‐SVM model is compared with the particle swarm algorithm optimisation SVM (PSO‐SVM) and genetic algorithm optimised SVM (GA‐SVM) models. Results show that the proposed model has a mean coefficient of determination of 95.65%, and the prediction performance is better than the PSO‐SVM and GA‐SVM model, indicating that the BES‐SVM model has good performance and is a feasible prediction method for soil water content prediction and guiding irrigation and fertilisation management in tea plantations.