Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A Review

Abstract

In recent years, agricultural robotics has received great attention in research studies, being considered a way to address some important issues of the agricultural sector, such as precision agriculture, resources saving, improvement of safety conditions, and shortage of human labor. These issues are particularly relevant in greenhouse production systems, where many highly repetitive and sometimes dangerous operations are still required to be performed by humans. The purpose of the present review is providing an overview of the research conducted in recent years related to robotic automation for greenhouse applications. The currently available literature about robots and automated solutions for greenhouse applications has been reviewed through the consultation of international databases of journals. A total of 38 publications were included after screening and the information related to each retrieved automated solution was classified. The research highlighted great variability among studies, which often describe automation solutions designed for specific crops and define the specific “supporting tasks” necessary for the completion of a “main task”. Specifically, the technologies used for guidance and navigation systems, crop detection and fruit grasping system, spraying system, and other minor supporting tasks have been described. Furthermore, a critical appraisal of the main challenges of the sector and future research directions are provided.

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

“…This is accomplished through a RANSAC [34] filtering process that iteratively formulates linear segments that effectively encapsulate the salient features within the 3D point cloud. RANSAC is a robust and widely adopted algorithm, renowned for its ability to identify these linear structures amidst potential noise and outliers within the data.The equation for the line model is denoted by Equation (5). The line coefficients comprise the coordinates of a point x 0 , y 0 , z 0 residing on the line, as well as the three-dimensional vector a, b, c that describes the line direction.…”

“…They provide a controlled environment that protects crops from the unpredictable fluctuations of the external climate and facilitates optimal conditions for crop growth, leading to extended growing seasons, increased yields, and reduced potential hazards associated with extreme climate-related occurrences, such as storms, frost, and excessive heat [4]. Moreover, greenhouses contribute to the establishment of a more secure and conducive working environment for agricultural workers [5]. Through the use of strategies such as limiting exposure to outdoor elements and employing controlled pest management techniques, the potential health hazards linked to chronic chemical exposure are mitigated.…”

Deep learning-empowered crop breeding: intelligent, efficient and promising

“…Most studies only report simulations, experiments, preliminary results, and specifications related to hardware/software design. In contrast, only a few studies discuss commercial solutions ( Bagagiolo et al., 2022 ). Additionally, the efficiency of the sensors and robots used is not high.…”

Work Efficiency Analysis of Multiple Heterogeneous Robots for Harvesting Crops in Smart Greenhouses

“…The ratio between the transporting speed of the harvesting robot, I T HR , and the transporting speed of the transporting robot, I TR in (4), is simplified as the ratio of the number of crops that the loading box can carry at one time, given as (9).…”

“…The growing process and harvesting conditions for each crop differ significantly, and the harvesting requirements in consideration of commerciality are also complex. Therefore, most studies are focused on the mechanism of a single harvesting robot [9].…”