Polyculture farming has environmental advantages but requires substantially more labor than monoculture farming. We present novel hardware and algorithms for automated pruning and irrigation. Using an overhead camera to collect data from physical$1.5~m^{2}$garden testbeds, 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 commercial gantry system, are experimentally evaluated. Irrigation is automated using soil moisture sensors. We present results for four 60-day garden cycles. Results suggest the system can autonomously achieve 94% normalized plant diversity with pruning shears while maintaining an average canopy coverage of 84% by the end of the cycles. For code, videos, and datasets, see https://sites.google.com/berkeley.edu/pruningpolyculturej/home.Note to Practitioners—While polyculture farming is closer to how plants grow in nature, it is considered more labor intensive that monoculture farming. In this paper we present approaches and custom hardware for automation of pruning and irrigation. Physical experiments suggest that automation can yield both high coverage and diversity.

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.edulsystematiccomparison

Polyculture farming is a sustainable farming technique based on synergistic interactions between differing plant types that make them more resistant to diseases and pests and better able to retain water. Reduced uniformity can reduce use of pesticides, fertilizer, and water, but is more labor intensive and more challenging to automate. We describe a scaled physical testbed (1.5m×3.0m) that uses a high resolution camera and soil sensors to monitor polyculture plants to facilitate tuning of plant growth, companion effects, and irrigation parameters for a first-order garden simulator. We use this simulator to develop a novel seed placement algorithm that increases coverage and diversity, and a learned pruning policy. In simulation experiments, the seed placement algorithm yields 60% more coverage and 10% more diversity than random seed placement and the learned pruning policy runs 1000X faster than a procedural lookahead policy to achieve high leaf coverage and plant diversity on adversarial gardens that include plant species with diverse growth rates. These models and policies provide the groundwork for a fully-automated system under development. Code, datasets and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden/.

In this paper an analysis on the use of Adaptive Coding and Modulation (ACM) techniques for EHF satellite communications is presented. In particular, our analysis is focused on W-band channels and includes the main channel impairments in this frequency band, i.e. rain fading and HPA non-linearity. The aim of the analysis is to identify modifications to the ACM mode switching algorithm to optimize their use at those high frequency bands.

The recently allocated 71-76 GHz and 81-86 GHz bands provide an opportunity for Line Of Sight (LOS) links for directional point-to-point “last mile” links. An efficient use of this spectrum may allow wireless to finally “catch up” with wires, leading to systems such as “multi-Gigabit wireless Ethernet,” and “wireless fiber.” However, the transmission at such a frequency range is characterized by several additional challenges compared to lower frequency bands, from the technological and propagation point of view, which makes difficult to use them efficiently. In this scenario, IR-UWB technology might offer some more degrees of freedom for the design of a highly integrated, low cost transceiver. This work has at its core the design and BER (Bit Error Rate) performance evaluation of an IR-UWB architecture based on an 85 GHz (this frequency belongs to W band/75-110 GHz) up-conversion stage of train of Gaussian pulses having a duration lower than 1 ns. Finally, we compare performance of this architecture with the ones of a more traditional continuous wave communications system with FSK (Frequency Shift Keying) modulation. Simulation results show that BER performance, in presence of RF non-linearities, for an IR-UWB transceiver architecture operating at W band (with same data rate and bandwidth) are better than a coherent BFSK scheme working in a similar scenario.