-- In this paper, we present a transformer-based method to spatio-temporally associate apple fruitlets in stereo-images collected on different days and from different camera poses. State-of-the-art association methods in agriculture are dedicated towards matching larger crops using either high-resolution point clouds or temporally stable features, which are both difficult to obtain for smaller fruit in the field. T o address these challenges, we propose a transformer-based architecture that encodes the shape and position of each fruitlet, and propagates and refines these features through a series of transformer encoder layers with alternating self and cross-attention. We demonstrate that our method is able to achieve an F1-score of 92.4% on data collected in a commercial apple orchard and outperforms all baselines and ablations. The global food supply is constantly under increasing pressure as a result of climate change, population growth, and increased labor shortages. To keep up with demand, agriculturalists are turning to computer vision-based systems that can automate a variety of laborious and time-intensive tasks such as harvesting [1], pruning [2], counting [3], and crop modeling [4]. These automated solutions not only improve efficiency, but also help mitigate the challenges posed by labor shortages and increasing food demand, ensuring that critical agricultural tasks can be performed reliably at scale. One particularly important but challenging task to automate is monitoring the growth and development of individual plants and fruits. Monitoring plant and fruit growth is important because it enables agricultural specialists to make more informed real-time crop management decisions and helps with downstream tasks such as phenotyping [5], disease management [6], and yield prediction [7].

This study performed an extensive evaluation of the performances of all configurations of YOLOv8, YOLOv9, and YOLOv10 object detection algorithms for fruitlet (of green fruit) detection in commercial orchards. Additionally, this research performed and validated in-field counting of fruitlets using an iPhone and machine vision sensors in 5 different apple varieties (Scifresh, Scilate, Honeycrisp, Cosmic crisp & Golden delicious). This comprehensive investigation of total 17 different configurations (5 for YOLOv8, 6 for YOLOv9 and 6 for YOLOv10) revealed that YOLOv9 outperforms YOLOv10 and YOLOv8 in terms of mAP@50, while YOLOv10x outperformed all 17 configurations tested in terms of precision and recall. Specifically, YOLOv9 Gelan-e achieved the highest mAP@50 of 0.935, outperforming YOLOv10n's 0.921 and YOLOv8s's 0.924. In terms of precision, YOLOv10x achieved the highest precision of 0.908, indicating superior object identification accuracy compared to other configurations tested (e.g. YOLOv9 Gelan-c with a precision of 0.903 and YOLOv8m with 0.897. In terms of recall, YOLOv10s achieved the highest in its series (0.872), while YOLOv9 Gelan m performed the best among YOLOv9 configurations (0.899), and YOLOv8n performed the best among the YOLOv8 configurations (0.883). Meanwhile, three configurations of YOLOv10: YOLOv10b, YOLOv10l, and YOLOv10x achieved superior post-processing speeds of 1.5 milliseconds, outperforming all other configurations within the YOLOv9 and YOLOv8 families. Specifically, YOLOv9 Gelan-e recorded a post-processing speed of 1.9 milliseconds, and YOLOv8m achieved 2.1 milliseconds. Furthermore, YOLOv8n exhibited the highest inference speed among all configurations tested, achieving a processing time of 4.1 milliseconds while YOLOv9 Gelan-t and YOLOv10n also demonstrated comparatively slower inference speeds of 9.3 ms and 5.5 ms, respectively.

In this paper, we present a computer vision-based approach to measure the sizes and growth rates of apple fruitlets. Measuring the growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops in order to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. With images collected by a hand-held stereo camera, our system, segments, clusters, and fits ellipses to fruitlets to measure their diameters. The growth rates are then calculated by temporally associating clustered fruitlets across days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3.5% of the current method with a 6 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps required to make the process fully autonomous.

In this paper, we present a next-best-view planning approach to autonomously size apple fruitlets. State-of-the-art viewpoint planners in agriculture are designed to size large and more sparsely populated fruit. They rely on lower resolution maps and sizing methods that do not generalize to smaller fruit sizes. To overcome these limitations, our method combines viewpoint sampling around semantically labeled regions of interest, along with an attention-guided information gain mechanism to more strategically select viewpoints that target the small fruits' volume. Additionally, we integrate a dual-map representation of the environment that is able to both speed up expensive ray casting operations and maintain the high occupancy resolution required to informatively plan around the fruit. When sizing, a robust estimation and graph clustering approach is introduced to associate fruit detections across images. Through simulated experiments, we demonstrate that our viewpoint planner improves sizing accuracy compared to state of the art and ablations. We also provide quantitative results on data collected by a real robotic system in the field.

Aotearoa New Zealand has a strong and growing apple industry but struggles to access workers to complete skilled, seasonal tasks such as thinning. To ensure effective thinning and make informed decisions on a per-tree basis, it is crucial to accurately measure the crop load of individual apple trees. However, this task poses challenges due to the dense foliage that hides the fruitlets within the tree structure. In this paper, we introduce the vision system of an automated apple fruitlet thinning robot, developed to tackle the labor shortage issue. This paper presents the initial design, implementation,and evaluation specifics of the system. The platform straddles the 3.4 m tall 2D apple canopy structures to create an accurate map of the fruitlets on each tree. We show that this platform can measure the fruitlet load on an apple tree by scanning through both sides of the branch. The requirement of an overarching platform was justified since two-sided scans had a higher counting accuracy of 81.17 % than one-sided scans at 73.7 %. The system was also demonstrated to produce size estimates within 5.9% RMSE of their true size.

Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards. One of the primary challenges is maintaining skilled human operators capable of making precise fruitlet thinning decisions. Thinning requires accurately measuring the true crop load for individual apple trees to provide optimal thinning decisions on an individual basis. A challenging task due to the dense foliage obscuring the fruitlets within the tree structure. This paper presents the initial design, implementation, and evaluation details of the vision system for an automatic apple fruitlet thinning robot to meet this need. The platform consists of a UR5 robotic arm and stereo cameras which enable it to look around the leaves to map the precise number and size of the fruitlets on the apple branches. We show that this platform can measure the fruitlet load on the apple tree to with 84% accuracy in a real-world commercial apple orchard while being 87% precise.