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

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 R2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.

Opportunities to improve the recommendation of plant varieties under the Recommended List (RL) system

“…Factor Analytic Linear Mixed Model (FA-LMM) is an improved method based on AMMI where it attempts to identify similar environments and reduce model complexity (Piepho 1997, Smith et al 2015, 2021). Lastly, given recent developments in Machine Learning (ML) and Artificial Intelligence (AI), there has been many ML/AI-based methods described for applications in variety recommendation system (Newman and Furbank 2021, Balakrishnan et al 2023, Hasan et al 2023, Han et al 2024, Shams et al 2024).…”

YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

“…In recent years, the fields of machine vision, deep learning (DL), and object detection have attracted much attention and achieved rapid development, including in the agricultural sector [25]. DL technology, distinguished using artificial neural networks (ANNs), is currently positioned at the forefront of advancements in weed detection [26].…”