Assessing the effectiveness of RS, GIS, and AI data integration in analysing agriculture performance to enable sustainable land management

The integration of Earth Remote Sensing (ERS) data with advancements in artificial intelligence has revolutionised sustainable land management. Current research in this field focuses on analysing remotely sensed data. This paper presents the results of effectively using spectral reflectance values of soil samples from several climatic zones in Kazakhstan to classify the content of macronutrients in soil, including nitrogen, phosphorus, potassium, and humus. The analysis of macronutrient content in the soil, combined with spectral data from Sentinel-2 L2A satellite imagery, has been integrated with geoinformation systems and mathematical modelling. The results of the macronutrient classification have been visualised in the form of cartograms. The classification analysis involved mathematical modelling of statistical data arrays on the content of phosphorus, potassium, humus, and nitrogen in the soil using the BN-BPNN neural network model, compared with data obtained from agrochemical soil sampling. The model tests demonstrate high efficiency for two soil types. For chernozem soil, the accuracy of nitrogen determination was 90.55%, phosphorus 98.1%, potassium 57.06%, and humus 90.54%. For chestnut soil, the accuracy was nitrogen 98.19%, phosphorus 42.16%, potassium 89.81%, and humus 98.88%. These results highlight the significant potential of this methodology for adaptation to various soil and climatic conditions. The “smart” technique developed for remote determination of macronutrient content, with automated express construction of cartograms, provides real-time information on soil nutrient levels. This research significantly enhances the integration efficiency of RS (remote sensing), GIS (geographical information systems), and AI (artificial intelligence) data in agriculture, contributing to sustainable land management.