Yield estimation and health assessment of temperate fruits: A modular framework

Yield estimation is crucial for growers and agronomists to optimize crop management practices and facilitate harvest planning. However, traditional manual fruit counting and fruit health assessment activities on large fields are labor-intensive, time-consuming, and prone to errors. Computer vision-based yield estimation methods involving fruit counting and health assessment using unmanned aerial vehicles (UAVs), have gained significant attention in recent years. This study proposes an automated yield estimation and health assessment approach through UAV imaging. Our methodology comprises three main components: (1) a robust fruit detection network based on the “you only look once - neural architecture search” (YOLONAS) model, (2) a fruit health assessment module to detect diseases in individually identified fruits, and (3) a post-processing and regression module for yield quantity and quality estimation. YOLONAS is a computationally efficient and accurate object detection model trained on scale-space augmented datasets. The health assessment module includes separable multiscale convolution layers with an additive attention module. We evaluated our yield estimation approach on three publicly available datasets featuring peach, apple, and citrus trees. Results reveal that YOLONAS, trained with a scale-space augmented dataset, improves detection accuracy by 1.2%. We also used a custom fruit disease dataset to assess the performance of the disease detection model, where we noticed that super-resolution of detected fruits with pre-trained models significantly enhances disease detection by up to 17%, especially in low-resolution fruits. Finally, we demonstrate that the proposed method can serve as a modular framework for yield quantity and quality assessment through UAVs in challenging field conditions.