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

Bagagiolo, Giorgia; Matranga, Giovanni; Pampuro, Niccolò

doi:10.3390/su14116436

Cited by 13 publications

(1 citation statement)

References 47 publications

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“…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.…”

Section: Challenges and Prospectsmentioning

confidence: 99%

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

Wang,

Zeng,

Lin

et al. 2023

Front. Plant Sci.

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Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.

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Section: Challenges and Prospectsmentioning

confidence: 99%

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

Wang,

Zeng,

Lin

et al. 2023

Front. Plant Sci.

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Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

Yao,

Bai,

Zhang

et al. 2024

Journal of Field Robotics

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The autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.

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