Xai-enhanced xgboost for crop recommendation using filter-wrapper based hybrid RF-PSOfeature selection for precision agriculture in Mizoram
173
Authors
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AMBETH KUMAR V D
ASSOCIATE PROFESSOR
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Zaitinkhuma
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Mohanty
Keywords:
machine learning, feature extraction, RF-PSO, XAI, precision farming, jhum cultivationAbstract
The study presents an XGBoost based machine learning (ML) model enhanced with explainable artificial intelligence (XAI) for crop recommendation system to assist the Mizo’s farmers
for selecting a suitable crop to grow on their farming land. A combine random forest (RF) and particle swarm optimization (PSO) feature extraction techniques (i.e., Hybrid RF + PSO) was proposed.
Synthetic minority over-sampling technique (SMOTE) was utilized to tackle the class imbalance issues in the dataset. SMOTE is employed to tackle class imbalances in the dataset. Moreover, the K-
fold cross validation (K = 5) was used during the training process to evaluate the model’s performance and ensure a comprehensive assessment of the training data. The XGBoost classifier trained on both
the RF top 8 features and Hybrid RF + PSO features selected, revealed that the proposed combine approach achieved a better outcome across the metrics considered although the cross validation standard deviation is slightly higher than RF top 8 features alone. Further, the precision recall (PR) curve analysis has shown that the proposed approach achieved a higher micro-average score than the RF tops 8 feature a lone. The analysis the confusion matrix revealed that, the proposed approach minimized an overall classification error than the RF top 8 features alone. Eventually, the model decision was interpreted using the SHAP (SHapley Additive exPlanations) to analyze the contribution of an individual feature towards the model decision. The SHAP analysis has revealed that, higher pH levels have a significant impact for rapeseed crop though it has a marginal contribution for other crop types. The features like nitrogen (N), and potassium (K) have an overall highest contribution across
the crops.
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