Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Abstract

Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field (652000-659000E-W and 4528000-4536000N-S) in 2019. This study's primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS (Geographical Information Systems) and deep learning (DL) technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88% was attained when classifying "class" data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

Keywords

• Deep learning
• Mapping
• Orzya sativa L.
• Paddy
• Precision agriculture

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