Modern high-input, intensive agricultural systems predominantly emphasize productivity and profitability at the expense of ecological balance. The Green Revolution, though instrumental in enhancing food security, relied heavily on mechanization, intensive cultivation, and high-yielding varieties, often compromising long-term sustainability. These practices have accelerated land use change and deforestation, leading to a substantial decline in soil organic matter (SOM), a reduction in terrestrial carbon sinks, and a rise in atmospheric carbon dioxide (CO₂) emissions. Under the increasing pressures of climate change—manifested in the form of drought, flooding, and pest outbreaks—the vulnerability of conventional farming systems has been exacerbated. In response to these challenges, regenerative organic agriculture (ROA) has been recognized as a holistic framework capable of restoring ecosystem functions, enhancing soil health, and supporting sustainable food production. This review synthesizes current research on ROA, with particular emphasis on practices that contribute to soil building and ecological regeneration. A meta-analysis of cover cropping practices across diverse soil types has demonstrated the potential to sequester soil organic carbon (SOC) between 0.32 and 16.70 Mg·ha⁻¹·yr⁻¹. Globally, an estimated SOC sequestration of 0.03 Pg·C·yr⁻¹ via cover crops could offset approximately 8% of anthropogenic greenhouse gas emissions. The physical, chemical, and biological improvements to soil properties facilitated by ROA have been systematically examined. Traditional Vedic agricultural practices in India have also been revisited for their ecological relevance and compatibility with regenerative principles. Integrated farming systems combining leguminous crops, agroforestry, horticulture, pasture, and animal husbandry have been reviewed for their synergistic effects on biodiversity enhancement, nutrient cycling, and climate mitigation. Additionally, the transition to renewable energy sources, reliance on self-saved seeds, and minimization of external inputs have been underscored as key strategies for achieving farm-level self-sufficiency and ecological sustainability. This review synthesizes scientific findings and traditional knowledge to highlight ROA as a holistic solution for restoring soil function, conserving natural resources, and advancing sustainable agricultural development.

The integration of artificial intelligence (AI) in precision agriculture has facilitated significant advancements in crop health monitoring, particularly in the early identification and classification of foliar diseases. Accurate and timely diagnosis of plant diseases is critical for minimizing crop loss and enhancing agricultural sustainability. In this study, an interpretable deep learning model—referred to as the Multi-Crop Leaf Disease (MCLD) framework—was developed based on a Convolutional Neural Network (CNN) architecture, tailored for the classification of tomato and grapevine leaf diseases. The model architecture was derived from the Visual Geometry Group Network (VGGNet), optimized to improve computational efficiency while maintaining classification accuracy. Leaf image datasets comprising healthy and diseased samples were employed to train and evaluate the model. Performance was assessed using multiple statistical metrics, including classification accuracy, sensitivity, specificity, precision, recall, and F1-score. The proposed MCLD framework achieved a detection accuracy of 98.40% for grapevine leaf diseases and a classification accuracy of 95.71% for tomato leaf conditions. Despite these promising results, further research is required to address limitations such as generalizability across variable environmental conditions and the integration of field-acquired images. The implementation of such interpretable AI-based systems is expected to substantially enhance precision agriculture by supporting rapid and accurate disease management strategies.