article · Neural Computing and Applications
Crop recommendation systems help farmers select suitable crops to optimise yields by analysing soil conditions, historical crop performance, and local weather patterns. However, conventional machine learning models often operate as opaque black boxes, limiting user trust and clarity. To address this issue, an algorithm called XAI-CROP integrates explainable artificial intelligence principles to deliver transparent, interpretable guidance. The algorithm was evaluated against several established machine learning approaches, including Gradient Boosting, Decision Tree, Random Forest, Gaussian Naïve Bayes, and Multimodal Naïve Bayes. Performance was assessed through Mean Squared Error, Mean Absolute Error, and R-squared metrics. The results showed that XAI-CROP achieved a Mean Squared Error of 0.9412, a Mean Absolute Error of 0.9874, and an R-squared value of 0.94152. These findings demonstrate that the system accurately predicts crop yield outcomes while explaining the vast majority of variability in the underlying agricultural data.
Farmers need reliable tools to choose the best crops for their specific soil and weather conditions, yet complex artificial intelligence models can be difficult to understand. By providing clear explanations alongside predictions, explainable tools can help agricultural workers understand the reasoning behind yield forecasts, fostering greater confidence in digital advisory services.
The algorithm could be integrated into precision agriculture software, farm advisory platforms, or digital extension tools used by farmers and agronomists. The work represents early-stage algorithmic development and testing against standard models on benchmark data, meaning software development, field trials, and user interface testing would be necessary before real-world deployment.
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Abstract 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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DOI: 10.1007/s00521-023-09391-2
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