Design and Implementation of an Urban Farming Robot.

Abstract

Urban agriculture can be shortly defined as the growing of plants and/or the livestock husbandry in and around cities. Although it has been a common occupation for the urban population all along, recently there is a growing interest in it both from public bodies and researchers, as well as from ordinary citizens who want to engage in self-cultivation. The modern citizen, though, will hardly find the free time to grow his own vegetables as it is a process that requires, in addition to knowledge and disposition, consistency. Given the above considerations, the purpose of this work was to develop an economic robotic system for the automatic monitoring and management of an urban garden. The robotic system was designed and built entirely from scratch. It had to have suitable dimensions so that it could be placed in a balcony or a terrace, and be able to scout vegetables from planting to harvest and primarily conduct precision irrigation based on the growth stage of each plant. Fertigation and weed control will also follow. For its development, a number of technologies were combined, such as Cartesian robots' motion, machine vision, deep learning for the identification and detection of plants, irrigation dosage and scheduling based on plants' growth stage, and cloud storage. The complete process of software and hardware development to a robust robotic platform is described in detail in the respective sections. The experimental procedure was performed for lettuce plants, with the robotic system providing precise movement of its actuator and applying precision irrigation based on the specific needs of the plants.

Plant phenotyping relevance

植物の成長段階を機械視覚・深層学習で識別し、その情報を用いて灌漑する都市農業ロボットの開発であり、植物状態の取得を含むロボット型フェノタイピング基盤が中心です。

Code and data availability

The paper describes a custom lettuce image dataset (400 images, ~6000 labelled plants) and a trained Faster-RCNN-Inception-V2 model, but no public deposit, repository, or availability statement for the dataset, images, trained model, or authors' code is provided. All referenced URLs are generic tools (OpenCV, Tensor-Fl

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