Matthew D. Whiting’s research while affiliated with Washington State University and other places

There is great interest in alternative pollination strategies for crop production in the face of climate change and perennial threats to the traditional pollination mechanisms. This review explores the potential for robotic pollination in response to these challenges to crop fertilization and global food production. Herein we describe the viability of novel robotic systems equipped with artificial intelligence and machine vision, as alternatives to traditional insect pollination. We examine the technological progress and challenges for both aerial and ground-based robotic artificial pollination systems and emphasize the need for continued research and development in this area to ensure sustainable agricultural productivity. This paper highlights the importance of robotic pollination as a practical and environmentally sustainable approach in modern agriculture, amidst burgeoning ecological threats and a dwindling agricultural workforce. Keywords: Robotic Pollination, Artificial Pollination, Automated Pollination, Pollination, Robotics, Agriculture

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.

Nanotechnology offers promising applications in agriculture and horticulture. Specifically, nanofertilizers (NFs) have been investigated for enhancing growth, antioxidant defense, and productivity in fruit-bearing plants. These crops are vital for supplying essential nutrients and minerals to humans. However, their production and quality often face challenges from various stresses. Using nanoparticles (NPs) can potentially mitigate these challenges, thereby improving the productivity and quality of horticulture crops. NPs possess unique chemical and physical properties that benefit plant growth, development, and stress tolerance, making them valuable for fruit crop enhancement. This review highlights recent advancements in employing nanoparticles to bolster fruit crop growth. Various nanoparticle types, such as metal oxide, metallic, carbon-based, and organic NPs have been demonstrated positive effects on plant abiotic stress tolerance growth and fruit quality. They have been found to boost nutrient absorption, neutralize free radicals, and activate plant stress response pathways, leading to enhanced quality and yield of fruit. This review aims to elucidate significant insights into the utilization of nanoparticles as a promising strategy for bolstering the resilience of horticultural plants and safeguarding food security in the face of environmental alterations. Notwithstanding the favorable outcomes observed in ameliorating plant performance under abiotic stresses, the molecular mechanisms underlying the beneficial effects of NPs remain a subject of ongoing investigation. Further research is imperative to delve into the enduring implications , safety considerations, and optimal techniques for the application of NPs in horticultural plants.

In order to meet the global food demand amidst the growing population, there’s a critical need to augment productivity in the specialty crop industry. However, workforce shortage in agriculture presents a significant challenge to increasing agricultural productivity, emphasizing the need for. automating various farm operations such as harvesting, canopy management, and crop-load management practices. A critical aspect of crop-load management is determining existing crop-load along with target yield, which is foundational for practices such as pruning and thinning. Buds, the initial phase of the flowering and fruiting process, are critical for early crop-load estimation(number of fruiting sites per branch or tree) and can facilitate informed pruning decisions to achieve the desired crop-load. Yet, the detection of these buds poses a huge challenge due to their miniature size and variable environmental conditions (including lighting) of commercial orchards. This research explores the performances of a Convolutional Neural Network (CNN) model and a VisionTransformer (ViT) model in detecting flower buds in apple orchards, using standard inference and a hyper inferencing technique. Additionally, the system quantifies buds and employs an association algorithm to correlate each bud to its respective branch, facilitating the estimation of the number of buds per branch. Despite the challenges due to the size of buds and environmental complexity, the proposed system demonstrated precision of 80.2%, recall of 80.1%, and f1-score of 80.2% in detecting flower buds, using a YOLOv8 model. Additionally, the system quantifies buds in a frame for the most salient branch using the branch-bud association with an accuracy of 73%. This study demonstrated a promising workflow in detecting and counting flower buds in a dynamic orchard environment to facilitate current manual pruning operation as well as future studies on automating pruning.

This research presents a novel, robotic pollination system designed for targeted pollination of apple flowers in modern fruiting wall orchards. Developed in response to the challenges of global colony collapse disorder, climate change, and the need for sustainable alternatives to traditional pollinators, the system utilizes a commercial manipulator, a vision system, and a spray nozzle for pollen application. Initial tests in April 2022 pollinated 56% of the target flower clusters with at least one fruit with a cycle time of 6.5 s. Significant improvements were made in 2023, with the system accurately detecting 91% of available flowers and pollinating 84% of target flowers with a reduced cycle time of 4.8 s. This system showed potential for precision artificial pollination that can also minimize the need for labor-intensive field operations such as flower and fruitlet thinning.

Dormant pruning is an important orchard activity for maintaining tree health and producing high-quality fruit. Due to decreasing worker availability, pruning is a prime candidate for robotics. However, pruning also represents a uniquely difficult problem, requiring robust systems for perception, pruning point determination, and manipulation that must operate under variable lighting conditions and in complex, highly unstructured environments. In this article, we introduce a system for pruning modern planar orchard architectures with simple pruning rules that combines various subsystems from our previous work on perception and manipulation. The integrated system demonstrates the ability to autonomously detect and cut pruning targets with minimal control of the environment, laying the groundwork for a fully autonomous system in the future. We validate the performance of our system through field trials in a sweet cherry orchard, ultimately achieving a cutting success rate of 58% across 10 trees. Though not fully robust and requiring improvements in throughput, our system is the first to operate on fruit trees and represents a useful base platform to be improved in the future.

Horticultural crop production is severely threatened by the changing global climate, as plants are vulnerable to a range of abiotic and biotic stresses. Abiotic stressors, such as floods, UV radiation, heat, drought, salt, and cold, often lead to crop damage and loss. However, plants have evolved sophisticated mechanisms enabling them to respond effectively to stressful conditions, thereby enhancing their ability to overcome challenges. They have evolved complex systems that detect stress signals and enable optimum growth responses, thus enabling them to survive in harsh environments. Due to their potential to mitigate the adverse impacts of abiotic stress, phytohormones have garnered significant research attention in recent years. Current findings have highlighted the crucial role that diverse phytohormones play in strengthening horticulture plants resistant to abiotic stress. Furthermore, plant growth regulators that function similarly to phytohormones, such as melatonin, have also been proven to be effective methods for mitigating the adverse impacts of both biotic and abiotic stress on fruit crops. A comprehensive understanding of the complex hormonal interactions that occur in a range of horticultural crops when exposed to biotic stress is a crucial summary of the function of phytohormones like plant growth regulators in decreasing abiotic stress and their associated crosstalk in the growth and development of plants under repeated stressful situations. The primary objective of this investigation is to focus on the fundamental advancements in the abiotic stress tolerance of fruit crops through the utilization of a diverse array of hormones including gibberellin (GA), brassinosteroids (BRs), abscisic acid (ABA), salicylic acid (SA), strigolactones (SLs), jasmonates (JAs), and melatonin (MEL). The insights gained from this study have the potential to facilitate sustainable plant growth, as hormones play a critical role in enhancing the abiotic stress resilience of horticultural crops.