The Role of Lightweight Vision Models in Enhancing Precision Pruning and Fruit Thinning Efficiency in Fruit Tree Cultivation

Abstract

The increasing demand for sustainable fruit production and efficient orchard management has accelerated the adoption of artificial intelligence (AI) and precision agriculture technologies in modern farming systems. Traditional pruning and fruit thinning practices in fruit tree cultivation are often labor-intensive, time-consuming, inconsistent, and highly dependent on skilled workers, resulting in rising operational costs and reduced productivity. In response to these challenges, lightweight vision models have emerged as promising smart agricultural technologies capable of supporting real-time image analysis, automated orchard monitoring, and precision pruning and fruit thinning operations. This study examines the role of lightweight vision models in enhancing pruning and fruit thinning efficiency within the context of sustainable fruit tree cultivation. Drawing upon the Diffusion of Innovation (DOI) Theory, Technology Acceptance Model (TAM), and sustainable agriculture perspectives, the study proposes a conceptual framework linking lightweight vision models, operational efficiency, and sustainable orchard management outcomes. The paper argues that lightweight AI-based vision systems can improve pruning accuracy, optimize fruit thinning processes, reduce labor dependency, enhance fruit quality, and increase agricultural productivity through intelligent and data-driven orchard management. In addition, the study highlights the potential contribution of lightweight vision technologies toward precision agriculture, resource optimization, and environmentally sustainable fruit cultivation practices. The findings are expected to provide valuable implications for policymakers, researchers, agricultural engineers, and fruit farmers in promoting smart orchard modernization and sustainable agricultural transformation. However, the applicability of lightweight vision systems should be interpreted as context-dependent, as differences in fruit species, canopy architecture, cultivation systems, environmental conditions, and pruning or thinning requirements may influence model performance and transferability.