Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Dutta, Ashit Kumar; Albagory, Yasser; Sait, Abdul Rahaman Wahab; Keshta, Ismail

doi:10.32604/cmc.2022.026783

Cited by 2 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning is centered on a far more complex image analysis process whereby meaningful features are automatically extracted from the raw input data, requiring relatively limited user input to develop, train, and evaluate the model to perform classifications. Deep learning models for weed mapping are usually based on some form of convolutional neural network (CNN), with the most popular example among the reviewed studies being the YOLO model [29,[41][42][43]. Despite their ability to produce highly accurate results and requiring relatively minimal user intervention, these models are complex, computationally demanding, and data-intensive, which may limit their feasibility for widespread crop and weed mapping applications.…”

Section: Algorithms and Methodologiesmentioning

confidence: 99%

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

Gokool,

Mahomed,

Clulow

et al. 2024

Drones

View full text Add to dashboard Cite

In light of a growing population and climate change compounding existing pressures on the agri-food system, there is a growing need to diversify agri-food systems and optimize the productivity and diversity of smallholder farming systems to enhance food and nutrition security under climate change. In this context, improving weed management takes on added significance, since weeds are among the primary factors contributing to crop yield losses for smallholder farmers. Adopting remote-sensing-based approaches to facilitate precision agricultural applications such as integrated weed management (IWM) has emerged as a potentially more effective alternative to conventional weed control approaches. However, given their unique socio-economic circumstances, there remains limited knowledge and understanding of how these technological advancements can be best utilized within smallholder farm settings. As such, this study used a systematic scoping review and attribute analysis to analyze 53 peer-reviewed articles from Scopus to gain further insight into remote-sensing-based IWM approaches and identify which are potentially best suited for smallholder farm applications. The findings of this review revealed that unmanned aerial vehicles (UAVs) are the most frequently utilized remote sensing platform for IWM applications and are also well suited for mapping and monitoring weeds within spatially heterogeneous areas such as smallholder farms. Despite the potential of these technologies for IWM, several obstacles to their operationalization within smallholder farm settings must be overcome, and careful consideration must be given on how best to maximize their potential before investing in these technologies.

show abstract

Section: Algorithms and Methodologiesmentioning

confidence: 99%

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

Gokool,

Mahomed,

Clulow

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…For weed detection tasks, more suitable convolutional neural network models include YOLO [123,124] series algorithms, SSD, Fast R-CNN, and Faster R-CNN. A new automated weed detection system was developed in [125] that uses convolutional neural network (CNN) classification to classify a real dataset of 4400 drone images containing 15,336 images.…”

Section: Algorithm Selectionmentioning

confidence: 99%

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Zhang,

Yu,

Zhang

et al. 2024

Agronomy

View full text Add to dashboard Cite

With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

show abstract

Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Cited by 2 publications

References 22 publications

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Contact Info

Product

Resources

About