Farmers’ acceptance of robotics and unmanned aerial vehicles: A systematic review

Degieter, Margo; Steur, Hans De; Tran, Duc; Gellynck, Xavier; Schouteten, Joachim J.

doi:10.1002/agj2.21427

Cited by 6 publications

(1 citation statement)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this scenario, using unmanned aerial vehicles (UAVs) [ 9 ] together with deep learning (DL) [ 10 , 11 , 12 ] represents an innovative solution in precision agriculture [ 13 ], especially for identifying fruits in orchards [ 14 , 15 ]. However, this combination comes with its own set of complexities.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning

Melnychenko,

Scislo,

Savenko

et al. 2024

Sensors

View full text Add to dashboard Cite

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning

Melnychenko,

Scislo,

Savenko

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Environmental performance of an autonomous laser weeding robot—a case study

Krupanek,

de Santos,

Emmi

et al. 2024

Int J Life Cycle Assess

View full text Add to dashboard Cite

Purpose Challenges in sustainable development envisioned in the European Union for the agricultural sector require innovation to raise the efficiency of production and safety of farming processes for farmers and ensure food safety for consumers. One of the key productivity factors in plant production is effective weeding. The WeLASER project aimed to develop a high-power autonomous vehicle with lasers to control weeds. To be sustainable, the invention should have a high environmental performance in the whole life cycle perspective, including its production, use in agriculture, and end-of-life phase. In the publication, a life cycle assessment (LCA) of the WeLASER weeding robot is presented. The aim was to identify weak and strong aspects of the invention in environmental terms and provide suggestions for its improvement. Methods The machinery was characterized based on technical data provided by the developers, relevant literature, Ecoinvent 3.8 database, and own calculations. The quantitative assessment of environmental impacts was performed using the Simapro tool. For interpretation Recipe 2016 method (egalitarian perspective) was applied. Results The results show that the energy issue related to autonomous laser-based weeding machine operations is the most challenging. It is related to impacts on climate change indicators and fossil fuel depletion. Production phase is characterized with impacts on human toxicity and is related to extensive application of electronic and electric components in the robot. Conclusion In comparison with other weeding techniques, the high-power autonomous vehicle with lasers to control weeds shows potential for environmentally efficient use of the machinery in practice. Achieving high performance in the life cycle perspective requires improvements in the design, operational features, and smart application in agricultural practice enhanced through expertise, guidance, and advice.

show abstract

Exploring farmers' perceptions of the technological characteristics of traceability systems

Fu,

Huang

2024

Agricultural Systems

View full text Add to dashboard Cite

Farmers’ acceptance of robotics and unmanned aerial vehicles: A systematic review

Cited by 6 publications

References 86 publications

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning

Environmental performance of an autonomous laser weeding robot—a case study

Exploring farmers' perceptions of the technological characteristics of traceability systems

Contact Info

Product

Resources

About