Applications and Advances in Fruit Crop Modeling: A Review

Abstract

Crop modeling serves as a powerful tool for optimizing fruit production by simulating tree responses to environmental factors like temperature, water, and light. Mechanistic models, particularly Functional-Structural Plant Models (FSPMs), enable 3D simulations of canopy dynamics, predicting light distribution, nutrient allocation, and fruit development. These models aid in designing optimal orchard layouts, improving irrigation and fertilization strategies, and mitigating pest and disease risks through targeted interventions. Additionally, FSPMs help predict fruit quality attributes such as sugar content and colour, allowing growers to fine-tune practices for market preferences. By integrating data-driven insights, crop modeling enhances yield, resource efficiency, and fruit quality, revolutionizing precision agriculture in fruit crops. However, crop models often face challenges due to incomplete data, complex interactions within systems, and lower accuracy when used outside their intended contexts. The review covers major modeling categories, including physiological growth models, crop-environment interaction models, climate-impact models, pest and disease models, water and nutrient management models, agroforestry models, and new machine-learning approaches focusing on recent advances, key applications and future directions. It highlights how these models improve prediction accuracy and support sustainable fruit production.