GradTabViTNet: drone-based multi-stage crop condition for cashew and cocoa yield estimation

Abstract

Precision agriculture is an essential approach to improving productivity, sustainability, and resilience in modern farming systems amid global changes. Drone-based monitoring represents a significant part of this agricultural transition because it can collect data across extensive regions and across multiple crop growth stages. Perennial cash crops are associated with farmers’ livelihoods and with links to global supply chains. Yield estimates for crops like cocoa and cashew are challenging because of the difficulty of measuring yields under very high canopy cover, erratic fruiting, and inefficient conventional in-field methodologies that involve excessive human error, labour, and time constraints. This paper proposes a hybrid deep learning model, GradTabViTNet, for multi-stage crop condition and yield estimation. The architecture integrates Vision Transformers (ViT) for geospatial attribute extraction, TabNet for attention-based tabular data analysis, CSRNet for accurate object counting in dense canopy environments, and Grad-CAM to enable interpretability by marking key regions in drone images. Classification and counting features are then combined using LightGBM regression to accurately estimate yield. Experimental evaluation using the cashew and cocoa datasets demonstrates that the proposed GradTabViTNet model outperforms existing methods, achieving 99.25% accuracy of 99.10%, precision 98.40%, recall, and 99.27% an F1-score of. The fusion of aerial monitoring with interpretable deep learning methods creates an extensible, stable approach for crop yield prediction, enabling sustainable agriculture, enhanced decision-making for farmers, and stronger food security through data-driven management of high-value perennial crops.

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