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Article · Chemical engineering transactions · September 1, 2022

Modelling of Forecasting Crop Yields Based on Earth Remote Sensing Data and Remote Sensing Methods

Marzhan Anuarbekovna Sadenova, Nail Beisekenov, Yerbolat Tleubekovich Ualiyev, N. A. Kulenova, Petar Sabev Varbanov

Nail Beisekenov
Read on cetjournal.itPDFDOI 10.3303/cet2294003

Abstract

In this work, the authors proposed a method of determining the yield of spring wheat based on the analysis of the dynamics of spectral parameters of its growth and development, determined by multispectral satellite images. It was found that by processing the satellite images of the fields in selected spectral regions, it is possible to estimate with a high degree of reliability the productivity of plants, biomass value, photosynthesis intensity and other parameters. By means of mathematical processing of the statistical data array of phosphorus, potassium and nitrogen content in the soil according to the Remote Sensing (RS) data in comparison with the actual data obtained after agrochemical analysis of soil samples, the total value of the average error (the average absolute error ranging from 24 to 36 % for the analysed period) was calculated. Using remote sensing data and Convolutional Neural Networks (CNN), the forecast of spring wheat yield in the conditions of soil and climatic zone of Eastern Kazakhstan was carried out. The results obtained with the predictive model are close to the actual yield results of the previous year (the error smaller than 9 %), indicating the relationship between yield and agrochemical analysis of the soil.

Remote Sensing in AgricultureSmart Agriculture and AIRemote Sensing and Land Usecrop yield predictionmultispectral satellite imageryconvolutional neural networks

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