This paper presents an aerially deployable crawler designed for adaptive locomotion and manipulation within tree canopies. The system combines compliant microspine-based tracks, a dual-track rotary gripper, and an elastic tail, enabling secure attachment and stable traversal across branches of varying curvature and inclination. Experiments demonstrate reliable gripping up to 90 degrees of body roll and inclination, while effective climbing on branches inclined up to 67.5 degrees, achieving a maximum speed of 0.55 body lengths per second on horizontal branches. The compliant tracks allow yaw steering of up to 10 degrees, enhancing maneuverability on irregular surfaces. Power measurements show efficient operation with a dimensionless cost of transport over an order of magnitude lower than typical hovering power consumption in aerial robots. Integrated within a drone-tether deployment system, the crawler provides a robust, low-power platform for environmental sampling and in-canopy sensing, bridging the gap between aerial and surface-based ecological robotics.

--We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation. Unlike prior approaches that rely on static scans or assume uniform orchard structures, our method adapts to varying field geometries via an integrated pipeline of LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction. We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns. A hybrid clustering strategy combining DBSCAN and spectral clustering enables robust per-tree segmentation, achieving 93% success in pistachio and 80% in almond, with strong agreement to drone-derived canopy volume estimates. Accurate estimation of tree canopy volume is fundamental to orchard management, with applications in yield prediction, biomass assessment, and optimized resource allocation [1].

Accurate estimation of total leaf area (TLA) is essential for assessing plant growth, photosynthetic activity, and transpiration but remains a challenge for bushy plants like dwarf tomatoes. Traditional destructive methods and imaging-based techniques often fall short due to labor intensity, plant damage, or the inability to capture complex canopies. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars-- Mohamed, Hahms Gelbe Topftomate, and Red Robin--grown under controlled greenhouse conditions. Two experiments, conducted in spring-summer and autumn-winter, included 73 plants, yielding 418 TLA measurements using an "onion" approach, where layers of leaves were sequentially removed and scanned. High-resolution videos were recorded from multiple angles for each plant, and 500 frames were extracted per plant for 3D reconstruction. Point clouds were created and processed, four reconstruction algorithms (Alpha Shape, Marching Cubes, Poisson's, and Ball Pivoting) were tested, and meshes were evaluated using seven regression models: Multivariable Linear Regression (MLR), Lasso Regression (Lasso), Ridge Regression (Ridge-Reg), Elastic Net Regression (ENR), Random Forest (RF), extreme gradient boosting (XGBoost), and Multilayer Perceptron (MLP). The Alpha Shape reconstruction (α = 3) combined with XGBoost yielded the best performance, achieving an R of 0.80 and MAE of 489 cm These findings demonstrate the robustness of our approach across variable environmental conditions and canopy structures. This scalable, automated TLA estimation method is particularly suited for urban farming and precision agriculture, offering practical implications for automated pruning, improved resource efficiency, and sustainable food production. Keywords: Total leaf area, dwarf tomato, point cloud, mesh reconstruction, machine learning, precision agriculture 1. Introduction Total leaf area (TLA) is a comprehensive metric describing the plant's growth and functioning. It is a primary metric that describes the plant's photosynthetic activity and transpiration capacity. Normalized by the plant's surface area, TLA may provide information on the canopy structure, which is crucial for understanding the plant's energy and resource efficiency. For example, reduced TLA is a sign of stress (Dong et al., 2019), while excessive biomass, indicated by a higher TLA, signifies lower water use efficiency (Glenn et al., 2006). Farmers often use pruning to reduce TLA in commercial crops to increase crop productivity (Budiarto et al., 2023). However, measuring and finding the optimum TLA of the crop are challenging tasks.

Abstract-- Due to labor shortages in specialty crop industries, a need for robotic automation to increase agricultural efficiency and productivity has arisen. Previous manipulation systems perform well in harvesting in uncluttered and structured environments. High tunnel environments are more compact and cluttered in nature, requiring a rethinking of the large form factor systems and grippers. We propose a novel codesigned framework incorporating a global detection camera and a local eye-in-hand camera that demonstrates precise localization of small fruits via closed-loop visual feedback and reliable error handling. Field experiments in high tunnels show our system can reach an average of 85.0% of cherry tomato fruit in 10.98s on average. I. INTRODUCTION Decreasing food miles and increasing sustainable agricultural practices have prompted interest in urban agriculture Figure 1: Robot picking cherry tomatoes with our Detect2Grasp in recent years.

This paper aims to make a mark in the future of sustainable robotics, where efficient algorithms are required to carry out tasks like environmental monitoring and precision agriculture efficiently. We proposed a hybrid algorithm that combines Artificial Bee Colony (ABC) with Levy flight to optimize adaptive sensor placement alongside an important notion of hotspots from domain knowledge experts. By enhancing exploration and exploitation, our approach significantly improves the identification of critical hotspots. This algorithm also finds its usecases for broader search and rescue operations applications, demonstrating its potential in optimization problems across various domains.

Forests offer essential resources and services to humanity, yet preserving and restoring them presents challenges, particularly due to the limited availability of actionable data, especially in hard-to-reach areas like forest canopies. Accessibility continues to pose a challenge for biologists collecting data in forest environments, often requiring them to invest significant time and energy in climbing trees to place sensors. This operation not only consumes resources but also exposes them to danger. Efforts in robotics have been directed towards accessing the tree canopy using robots. A swarm of drones has showcased autonomous navigation through the canopy, maneuvering with agility and evading tree collisions, all aimed at mapping the area and collecting data. However, relying solely on free-flying drones has proven insufficient for data collection. Flying drones within the canopy generates loud noise, disturbing animals and potentially corrupting the data. Additionally, commercial drones often have limited autonomy for dexterous tasks where aerial physical interaction could be required, further complicating data acquisition efforts. Aerial deployed sensor placement methods such as bio-gliders and sensor shooting have proven effective for data collection within the lower canopy. However, these methods face challenges related to retrieving the data and sensors, often necessitating human intervention.

Modern day studies show a high degree of correlation between high yielding crop varieties and plants with upright leaf angles. It is observed that plants with upright leaf angles intercept more light than those without upright leaf angles, leading to a higher rate of photosynthesis. Plant scientists and breeders benefit from tools that can directly measure plant parameters in the field i.e. on-site phenotyping. The estimation of leaf angles by manual means in a field setting is tedious and cumbersome. We mitigate the tedium using a combination of the Mask R-CNN instance segmentation neural network, and Line Segment Transformer (LETR), a vision transformer. The proposed Computer Vision (CV) pipeline is applied on two image datasets, Summer 2015-Ames ULA and Summer 2015- Ames MLA, with a combined total of 1,827 plant images collected in the field using FieldBook, an Android application aimed at on-site phenotyping. The leaf angles estimated by the proposed pipeline on the image datasets are compared to two independent manual measurements using ImageJ, a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation. The results, when compared for similarity using the Cosine Similarity measure, exhibit 0.98 similarity scores on both independent measurements of Summer 2015-Ames ULA and Summer 2015-Ames MLA image datasets, demonstrating the feasibility of the proposed pipeline for on-site measurement of leaf angles.

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.

Aerial robots show significant potential for forest canopy research and environmental monitoring by providing data collection capabilities at high spatial and temporal resolutions. However, limited flight endurance hinders their application. Inspired by natural perching behaviours, we propose a multi-modal aerial robot system that integrates tensile perching for energy conservation and a suspended actuated pod for data collection. The system consists of a quadrotor drone, a slewing ring mechanism allowing 360{\deg} tether rotation, and a streamlined pod with two ducted propellers connected via a tether. Winding and unwinding the tether allows the pod to move within the canopy, and activating the propellers allows the tether to be wrapped around branches for perching or disentangling. We experimentally determined the minimum counterweights required for stable perching under various conditions. Building on this, we devised and evaluated multiple perching and disentangling strategies. Comparisons of perching and disentangling manoeuvres demonstrate energy savings that could be further maximized with the use of the pod or tether winding. These approaches can reduce energy consumption to only 22\% and 1.5\%, respectively, compared to a drone disentangling manoeuvre. We also calculated the minimum idle time required by the proposed system after the system perching and motor shut down to save energy on a mission, which is 48.9\% of the operating time. Overall, the integrated system expands the operational capabilities and enhances the energy efficiency of aerial robots for long-term monitoring tasks.

Soybean production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, i.e. drought, emerges as a significant risk for soybean production, underscoring the need for advancements in stress monitoring for crop breeding and production. This project combines multi-modal information to identify the most effective and efficient automated methods to investigate drought response. We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress. We utilized high-throughput time-series phenotyping using UAVs and sensors in conjunction with machine learning (ML) analytics, which offered a swift and efficient means of phenotyping. The red-edge and green bands were most effective to classify canopy wilting stress. The Red-Edge Chlorophyll Vegetation Index (RECI) successfully differentiated susceptible and tolerant soybean accessions prior to visual symptom development. We report pre-visual detection of soybean wilting using a combination of different vegetation indices. These results can contribute to early stress detection methodologies and rapid classification of drought responses in screening nurseries for breeding and production applications.