A mixed-autonomous robotic platform for intra-row and inter-row weed removal for precision agriculture

Visentin, Francesco; Cremasco, Simone; Sozzi, Marco; Signorini, Luca; Signorini, Moira; Marinello, Francesco; Muradore, Riccardo

doi:10.1016/j.compag.2023.108270

Cited by 22 publications

(4 citation statements)

References 24 publications

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“…The model's high inference speed of 15.1 FPS further enhances its significance for real-time, accurate crop and weed segmentation, aiding the development of intelligent agricultural equipment. More recently, Visentin et al (2023) [120] combined remote human-controlled rover motion with a pretrained Deep Neural Network (DNN) for autonomous weed identification and removal. These advancements contribute to the development of intra-row weeders, enhancing their precision and efficiency, and paving the way for more sustainable weed management strategies in agriculture.…”

Section: Advancements In Machine Learning For Weed Detectionmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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Sensor-based weed detection is a significant breakthrough in precise weed management that reduces the need for manual work and herbicides. GPS systems have shown promising results, with a single pass covering 30-49% of the intra-row area. Additionally, using an RGB sensor and a laser sensor together can identify the center of a plant, regardless of lighting conditions. Robots have demonstrated the ability to distinguish 99.7% of crop plants in dense outdoor areas with high weed density. These advancements offer opportunities for more effective and environmentally-friendly weed management in different farming systems.

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Section: Advancements In Machine Learning For Weed Detectionmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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“…Intra-row WEP with a vertical rotation design is more common, such as disc-hoe WEP (Garford, 2023), and the cycloid hoe WEP (Norremark et al, 2008(Norremark et al, , 2012, etc. Garford (2023) designed a disc-hoe with an arc-shaped notch, this kind of WEP realized weeding by rotating the notched disc knife, and the seedling avoidance function is realized by a control program, which lets the notch towards the seedling all the time when a crop is detected. Since the notched disc knife is stretched into the soil all the time, along with the forward of the power machine, it achieves weeding around the crop.…”

Section: Dry Fields Wepmentioning

confidence: 99%

“…Since crops are almost mechanically planted in rows nowadays, their positions are more uniform then weeds, so the latter scenario is more cost-effective in real applications. Such as the designs of Quan et al (2022); Garford (2023); Poulsen (2023b) and the German company K.U.L.T. Kress (Kress, 2023) were all applied this designing principle.…”

Section: Introductionmentioning

confidence: 99%

Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review

Xiang,

Qu,

Wang

et al. 2024

Front. Plant Sci.

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Weeding is a key link in agricultural production. Intelligent mechanical weeding is recognized as environmentally friendly, and it profoundly alleviates labor intensity compared with manual hand weeding. While intelligent mechanical weeding can be implemented only when a large number of disciplines are intersected and integrated. This article reviewed two important aspects of intelligent mechanical weeding. The first one was detection technology for crops and weeds. The contact sensors, non-contact sensors and machine vision play pivotal roles in supporting crop detection, which are used for guiding the movements of mechanical weeding executive parts. The second one was mechanical weeding executive part, which include hoes, spring teeth, fingers, brushes, swing and rotational executive parts, these parts were created to adapt to different soil conditions and crop agronomy. It is a fact that intelligent mechanical weeding is not widely applied yet, this review also analyzed the related reasons. We found that compared with the biochemical sprayer, intelligent mechanical weeding has two inevitable limitations: The higher technology cost and lower working efficiency. And some conclusions were commented objectively in the end.

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“…It provides a comprehensive look at how AI can be implemented for more efficient crop production and monitoring while minimizing ecological footprints. Complementing these findings, Visentin et al (2023) [9] investigate a mixed-autonomous robotic platform for precise weed removal in both intra-row and inter-row settings. This development underscores the role of AI in precision agriculture, illustrating how robotic systems can be specialized for tasks like exact weed control, which helps in reducing chemical herbicide reliance and supports sustainable agriculture.…”

Section: Introductionmentioning

confidence: 99%

Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm

Cob-Parro,

Lalangui,

Lazcano

2024

Agronomy

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As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this “land gap” and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle.

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A mixed-autonomous robotic platform for intra-row and inter-row weed removal for precision agriculture

Cited by 22 publications

References 24 publications

Advancements in precision control in intra-row weeding: A comprehensive review

Advancements in precision control in intra-row weeding: A comprehensive review

Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review

Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm

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