Soil Properties Classification in Sustainable Agriculture Using Genetic Algorithm-Optimized and Deep Neural Networks

Yadviga Tynchenko, Vadim Tynchenko (), Vladislav Kukartsev, Tatyana Panfilova, Oksana Kukartseva, Ksenia Degtyareva, Nguyen Van and Ivan Malashin ()
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Yadviga Tynchenko: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vadim Tynchenko: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vladislav Kukartsev: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Tatyana Panfilova: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
Oksana Kukartseva: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
Ksenia Degtyareva: Center for Continuing Education, Bauman Moscow State Technical University, 105005 Moscow, Russia
Nguyen Van: Institute of Energy and Mining Mechanical Engineering—Vinacomin, Hanoi 100000, Vietnam
Ivan Malashin: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

Sustainability, 2024, vol. 16, issue 19, 1-28

Abstract: Optimization of land management and agricultural practices require precise classification of soil properties. This study presents a method to fine-tune deep neural network (DNN) hyperparameters for multiclass classification of soil properties using genetic algorithms (GAs) with knowledge-based generation of hyperparameters. The focus is on classifying soil attributes, including nutrient availability (0.78 ± 0.11), nutrient retention capacity (0.86 ± 0.05), rooting conditions (0.85 ± 0.07), oxygen availability to roots (0.84 ± 0.05), excess salts (0.96 ± 0.02), toxicity (0.96 ± 0.01), and soil workability (0.84 ± 0.09), with these accuracies representing the results from classification with variations from cross-validation. A dataset from the USA, which includes land-use distribution, aspect distribution, slope distribution, and climate data for each plot, is utilized. A GA is applied to explore a wide range of hyperparameters, such as the number of layers, neurons per layer, activation functions, optimizers, learning rates, and loss functions. Additionally, ensemble methods such as random forest and gradient boosting machines were employed, demonstrating comparable accuracy to the DNN approach. This research contributes to the advancement of precision agriculture by providing a robust machine learning (ML) framework for accurate soil property classification. By enabling more informed and efficient land management decisions, it promotes sustainable agricultural practices that optimize resource use and enhance soil health for long-term ecological balance.

Keywords: soil; machine learning; land management; soil quality; sustainable land management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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