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Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Author

Listed:
  • Ivan Malashin

    (Artificial Intelligence Technology Scientific and Education Center, 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)

  • Andrei Gantimurov

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Vladimir Nelyub

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia)

  • Aleksei Borodulin

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Yadviga Tynchenko

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

Abstract

Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various agricultural and climatic variables are included in the analysis, encompassing crop type, year, season, and the specific climatic conditions of the Indian state during the crop’s growing season. Features such as crop and season were one-hot encoded. The primary objective was to predict yield using a deep neural network (DNN), with hyperparameters optimized through genetic algorithms (GAs) to maximize the R 2 score. The best-performing model, achieved by fine-tuning its hyperparameters, achieved an R 2 of 0.92, meaning it explains 92% of the variation in crop yields, indicating high predictive accuracy. The optimized DNN models were further analyzed using explainable AI (XAI) techniques, specifically local interpretable model-agnostic explanations (LIME), to elucidate feature importance and enhance model interpretability. The analysis underscored the significant role of features such as crops, leading to the incorporation of an additional dataset to classify the most optimal crops based on more detailed soil and climate data. This classification task was also executed using a GA-optimized DNN, aiming to maximize accuracy. The results demonstrate the effectiveness of this approach in predicting crop yields and classifying optimal crops.

Suggested Citation

  • Ivan Malashin & Vadim Tynchenko & Andrei Gantimurov & Vladimir Nelyub & Aleksei Borodulin & Yadviga Tynchenko, 2024. "Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools," Sustainability, MDPI, vol. 16(21), pages 1-29, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9437-:d:1510298
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    References listed on IDEAS

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