<div>COVID-19 has threatened agri-food sector in countless ways. Various intellectuals fear that if it prevails for long, it might lead to shortage of agricultural inputs coupled with labor shortages, stressed supply chains, price hikes and food insecurity. This paper adds to the existing literature in the agri-business domain through unraveling the imbroglio of similar challenges and shedding light on the practical solutions which shall ultimately pave path for better agricultural systems. It further illustrates how important it is for the governments to bat for the emerging opportunities and score for the robust, efficient and resilient future with the help of QDA software Atlas.ti.</div>

Agri-food Systems Amidst COVID-19: A Review

Background: Mixed-cropping are widely recognized for enhancing land-use efficiency, improving soil fertility and contributing to long-term agricultural sustainability. However, choosing optimal crop combinations tailored to specific farm conditions remains a challenging, particularly in developing regions. 
 
Methods: To address this limitation, this study proposes a novel explainable artificial intelligence (XAI)-Driven hybrid machine learning and ant colony optimization (ACO) framework (XAI-HACO) for mixed-crop recommendation, integrating soil nutrient, weather variables. hybrid machine learning techniques such as random forest-extra trees (RF-ET), decision tree-C4.5 (DT-C4.5), extreme gradient boosting-gradient boosting (XGBoost-GBoost), quadratic discriminant analysis-linear discriminant analysis (QDA-LDA) and support vector machine-stochastic gradient decent (SVM-SGD) were developed and assessed using an indigenous mixed-crop dataset of Andhra Pradesh, India. The models performance was assessed using accuracy, precision, recall, F1-score, confusion matrix and ROC-Curve, while ACO was employed to optimize feature selection and model hyperparameters. 
 
Result: The investigational results show that the proposed RF-ET hybrid model achieved superior performance 95.91% accuracy, precision at 95.08%, recall at 95.91% and F1-score at 95.49%. These results show that the proposed XAI-HACO framework offers a reliable, transparent, interpretability and data-driven decision support tool for farmers and agricultural stakeholders, facilitating informed selection of suitable mixed-cropping systems. 
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Agri-food Systems Amidst COVID-19: A Review

Explainable AI-enabled Mixed-crop Recommendation Model using Hybrid Machine Learning and Ant Colony Optimization