Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide persona...

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Vydáno v:Neural computing & applications Ročník 36; číslo 11; s. 5695 - 5714
Hlavní autoři: Shams, Mahmoud Y., Gamel, Samah A., Talaat, Fatma M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.04.2024
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Crop yield
Data Mining and Knowledge Discovery
Decision making
Decision trees
Explainable artificial intelligence
Harnesses
Image Processing and Computer Vision
Machine learning
Original Article
Performance evaluation
Probability and Statistics in Computer Science
Recommender systems
Agriculture
Crop recommendation systems
eXplainable artificial intelligence
Decision support system
Machine learning
ISSN:0941-0643, 1433-3058
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Shrnutí:Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R 2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09391-2