A Robotic System for Precision Pollination in Apples: Design, Development and Field Evaluation

Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to different factors, including climate change, habitat loss, and pesticide use. Thus, developing alternative pollination methods is essential for sustainable crop production. This paper introduces a robotic system for precision pollination in apples, which are not self-pollinating and require precise delivery of pollen to the stigmatic surfaces of the flowers. The proposed robotic system consists of a machine vision system to identify target flowers and a mechatronic system with a 6-DOF UR5e robotic manipulator and an electrostatic sprayer. Field trials of this system in 'Honeycrisp' and 'Fuji' apple orchards have shown promising results, with the ability to pollinate flower clusters at an average spray cycle time of 6.5 seconds. The robotic pollination system has achieved encouraging fruit set and quality, comparable to naturally pollinated fruits in terms of color, weight, diameter, firmness, soluble solids, and starch content. However, the results for fruit set and quality varied between different apple cultivars and pollen concentrations. This study demonstrates the potential for a robotic artificial pollination system to be an efficient and sustainable method for commercial apple production. Further research is needed to refine the system and assess its suitability across diverse orchard environments and apple cultivars.

Machine Vision Based Assessment of Fall Color Changes in Apple Trees: Exploring Relationship with Leaf Nitrogen Concentration

Apple trees being deciduous trees, shed leaves each year which is preceded by the change in color of leaves from green to yellow (also known as senescence) during the fall season. The rate and timing of color change are affected by the number of factors including nitrogen (N) deficiencies. The green color of leaves is highly dependent on the chlorophyll content, which in turn depends on the nitrogen concentration in the leaves. The assessment of the leaf color can give vital information on the nutrient status of the tree. The use of a machine vision based system to capture and quantify these timings and changes in leaf color can be a great tool for that purpose. \par This study is based on data collected during the fall of 2021 and 2023 at a commercial orchard using a ground-based stereo-vision sensor for five weeks. The point cloud obtained from the sensor was segmented to get just the tree in the foreground. The study involved the segmentation of the trees in a natural background using point cloud data and quantification of the color using a custom-defined metric, \textit{yellowness index}, varying from $-1$ to $+1$ ($-1$ being completely green and $+1$ being completely yellow), which gives the proportion of yellow leaves on a tree. The performance of K-means based algorithm and gradient boosting algorithm were compared for \textit{yellowness index} calculation. The segmentation method proposed in the study was able to estimate the \textit{yellowness index} on the trees with $R^2 = 0.72$. The results showed that the metric was able to capture the gradual color transition from green to yellow over the study duration. It was also observed that the trees with lower nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen. The onset of color transition during both years aligned with the $29^{th}$ week post-full bloom.