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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., Comment: International Conference on Robotics and Automation(ICRA) 2023 Oral

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., Comment: CASE 2022, 8 pages. arXiv admin note: substantial text overlap with arXiv:2111.06014

This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1.5m x 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. We present results for four 60-day garden cycles. 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. Code, datasets, and supplemental material can be found at https://github.com/BerkeleyAutomation/AlphaGarden., Comment: Paper revised, extended, and resubmitted. See "Automated Pruning of Polyculture Plants."

Spectral efficiency analysis in presence of correlated interfering signals is very important in modern generation wireless networks where there is aggressive frequency reuse with a dense deployment of access points. However, most works available in literature either address the effect of correlated interfering signals or include interferer activity, but not both. Further, available literature has also addressed the effect of large-scale fading (shadowing and distance-dependent path loss) only, however, has fallen short of including the composite effect of the line of sight and non-line of sight small-scale fading. The correlation of desired signals with interfering signals due to shadowing has also not been considered in existing literature. In this work, we present a comprehensive analytical signal to interference power ratio evaluation framework addressing all the above mentioned important components to the model in a holistic manner. In this analysis, we extend and apply the Moment Generating Function-matching method to such systems so that correlation and activity of lognormal random variables can be included with high accuracy. We compare the analytical results against realistic channel model based extensive Monte-Carlo simulation for mmWave and sub-6 GHz in both indoor and outdoor scenarios. the performance of the model is depicted in terms of mean, alpha-percentile outage spectral efficiency and Kullback-Leibler divergence and Kolmogorov-Smirnov distance., Comment: Published in IEEE Transactions on Vehicular Technology