-- The small scale of urban farms and the commercial availability of low-cost robots (such as the FarmBot) that automate simple tending tasks enable an accessible platform for plant phenotyping. We have used a FarmBot with a custom camera end-effector to estimate strawberry plant flower pose (for robotic pollination) from acquired 3D point cloud models. We describe a novel algorithm that translates individual occupancy grids along orthogonal axes of a point cloud to obtain 2D images corresponding to the six viewpoints. For each image, 2D object detection models for flowers are used to identify 2D bounding boxes which can be converted into the 3D space to extract flower point clouds. Pose estimation is performed by fitting three shapes (superellipsoids, paraboloids and planes) to the flower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. Urban farms [1] provide healthy food to local communities and can serve as platforms for education and sustainability.

Urban gardening is widely recognized for its numerous health and environmental benefits. However, the lack of suitable garden spaces, demanding daily schedules and limited gardening expertise present major roadblocks for citizens looking to engage in urban gardening. While prior research has explored smart home solutions to support urban gardeners, these approaches currently do not fully address these practical barriers. In this paper, we present PlantPal, a system that enables the cultivation of garden spaces irrespective of one's location, expertise level, or time constraints. PlantPal enables the shared operation of a precision agriculture robot (PAR) that is equipped with garden tools and a multi-camera system. Insights from a 3-week deployment (N=18) indicate that PlantPal facilitated the integration of gardening tasks into daily routines, fostered a sense of connection with one's field, and provided an engaging experience despite the remote setting. We contribute design considerations for future robot-assisted urban gardening concepts.

Three robots are growing vegetables on the roof of the University of Melbourne's student pavilion. As I watch, a mechanical arm, hovering above the crop like a fairground claw machine, sprays a carefully measured dose of water over the plants. The greens themselves look fairly terrestrial – cos lettuce, basil, coriander and moth-eaten kale – but they are actually prototypes for a groundbreaking research mission to grow fresh food in outer space. The project leader, Prof Sigfredo Fuentes, leans over and picks a tiny caterpillar from a kale leaf. "We had a real plague of cabbage moths last week, but it's OK; the kale's just here to distract them from the other vegetables." Prof Fuentes is part of the wonderfully named Australian Research Council Centre of Excellence in Plants for Space – a seven-year collaboration between five Australian universities – which has partnered with 38 organisations, including Nasa, to crack the code of fresh, nutritious "space food".

The AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms. In this paper, we systematically compare the performance of the AlphaGarden to professional horticulturalists on the staff of the UC Berkeley Oxford Tract Greenhouse. The humans and the machine tend side-by-side polyculture gardens with the same seed arrangement. We compare performance in terms of canopy coverage, plant diversity, and water consumption. Results from two 60-day cycles suggest that the automated AlphaGarden performs comparably to professional horticulturalists in terms of coverage and diversity, and reduces water consumption by as much as 44%. Code, videos, and datasets are available at https://sites.google.com/berkeley.edu/systematiccomparison.

This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture, a collaboration among landscape architects, architects and computer scientists who specialize in artificial intelligence, robotics and computer vision. ELUA has two gantry robots, one indoors and the other outside on the rooftop of a 6-story campus building. Each robot can seed, water, weed, and prune in its garden. To support responsive landscape research, ELUA also includes sensor arrays, an AI-powered camera, and an extensive network infrastructure. This project demonstrates a way to integrate artificial intelligence into an evolving urban ecosystem, and encourages landscape architects to develop an adaptive design framework where design becomes a long-term engagement with the environment.

Polyculture farming has environmental advantages but requires substantially more pruning than monoculture farming. We present novel hardware and algorithms for automated pruning. Using an overhead camera to collect data from a physical scale garden testbed, the autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day. From this garden state, AlphaGardenSim selects plants to autonomously prune. A trained neural network detects and targets specific prune points on the plant. Two custom-designed pruning tools, compatible with a FarmBot gantry system, are experimentally evaluated and execute autonomous cuts through controlled algorithms. We present results for four 60-day garden cycles. Results suggest the system can autonomously achieve 0.94 normalized plant diversity with pruning shears while maintaining an average canopy coverage of 0.84 by the end of the cycles. For code, videos, and datasets, see https://sites.google.com/berkeley.edu/pruningpolyculture.

Abstract-- This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1.5m 3.0m physical testbed. AlphaGarden uses an overhead camera and sensors to track the plant distribution and soil moisture. We model individual plant growth and interplant dynamics to train a policy that chooses actions to maximize leaf coverage and diversity. For autonomous pruning, AlphaGarden uses two custom-designed pruning tools and a trained neural network to detect prune points. Results suggest AlphaGarden can autonomously achieve 0.96 normalized diversity with pruning shears while maintaining an average canopy coverage of 0.86 during the peak of the cycle. Industrial agriculture is based on monoculture, where a single crop type is cultivated, requiring substantial use of, pesticides, and water [1], [2].

Researchers at the University of North Carolina's Center for Geospatial Analytics (CGA) are using artificial intelligence (AI) and machine learning (ML) to help farmers better adapt their crops to changing climates. Speaking to ZDNet, CGA associate director Ranga Raju Vatsavai said his team of researchers has been working in partnership with Lenovo for the last two years to develop AI and ML solutions to help farmers preemptively identify ways to best optimise water and energy -- and ultimately address the threats to food insecurity. "Our area of research is to extract actionable knowledge from the datasets. Food, energy, and water are a good application because the population is going to reach 10 billion by 2050. Right now, we are utilising 70% of fresh water for agriculture," he said.

Last week a startup based at Plymouth University unveiled the world's first raspberry-picking robot. The machine can pick about 25,000 berries a day, which is 10,000 more than a human during an eight-hour shift. Raspberries are particularly challenging for machines to harvest because the robots have to identify ripe fruit and handle the soft berries without damaging them. The firm intends to lease the robots to farmers at a rate that would undercut the cost of employing human fruit pickers. Last month farming startup Iron Ox began selling salad partly farmed by robots at a store in California.

Agriculture is seeing accelerated adoption of Artificial Intelligence (AI) and Machine Learning (ML) the two with regard to agricultural products and in-field farming tactics. Cognitive computing, particularly, is set to turn into the most disruptive technology in agriculture services as it can certainly understand, learn, and answer various conditions (predicated on studying) to improve efficiency. Technology may likewise be used to recognize optimal sowing period, historical weather information, real time Moisture Adequacy info (MAI) from everyday rainfall and soil contamination to make predictability and supply inputs to farmers at ideal sowing time. To determine likely pest attacks, Microsoft in cooperation with United Phosphorus Limited is currently building a Pest danger Prediction API that ignites AI and machine understanding how to signify in progress, and the risk of pest attack. Predicated around harvest growth period and the weather illness, pest attacks are called to Moderate, High or lower.