UAV Image Based Crop and Weed Distribution Estimation on Embedded GPU Boards

Fawakherji, Mulham; Potena, Ciro; Bloisi, Domenico Daniele; Imperoli, Marco; Pretto, Alberto; Nardi, Daniele

doi:10.1007/978-3-030-29930-9_10

Cited by 20 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fawakherji et al. (2019) proposed approaches for crop and weed mapping, for example. In the future, it may become possible that crop damage and the location within the field can be detected directly while flying over the field.…”

Section: Discussionmentioning

confidence: 99%

Combined UAV‐ and tractor‐based stripe rust monitoring in winter wheat under field conditions

et al. 2021

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) have the potential to monitor the health status of several crop fields of a farmer in full coverage. For a complete and fast monitoring, however, high flight altitudes are usually needed, especially if large areas should be observed in short time intervals. In this case, the resolution on the ground becomes insufficient to detect specific symptoms of crop diseases because a ground resolution in the submillimeter scale is needed. This study pursued the idea of performing remote UAV imaging to detect discoloration in combination with near‐surface tractor imaging to detect the uredospore layers as characteristic signs of stripe (yellow) rust (caused by Puccinia striiformis Westend. f. sp. tritici) in winter wheat (Triticum aestivum L.). To simulate healthy and diseased field parts, the 3‐yr experimental design included controlled infected plots and those sprayed with fungicides as healthy controls. Imaging, disease severity, and crop development rating were performed along a time series. Significant differences between infected and control plots occurred in the UAV imagery using the normalized green–red difference index from a median (upper three leaves) infested leaf area of 3% and for the tractor images using the maximally stable extreme regions detector from 3 and 5%, respectively. In the future, it is conceivable that farmers will combine UAV (aerial monitoring of crop damage of complete fields) and tractor (ground monitoring to determine the cause) imaging for automatic scanning of the health status.

show abstract

Section: Discussionmentioning

confidence: 99%

Combined UAV‐ and tractor‐based stripe rust monitoring in winter wheat under field conditions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Indeed, the three camera types can recognize weed patches with good accuracy depending on flying altitude, camera resolution and UAV used. UAVs have been mainly tested on important crops such as Triticum spp., Hordeum vulgare, Beta vulgaris, Zea mays [98][99][100][101]. These are among the most cultivated crops worldwide and are highly susceptible to weed competition especially in early phenological stages.…”

Section: Applications Of Uavs To Weed Managementmentioning

confidence: 99%

Drone and sensor technology for sustainable weed management: a review

Esposito

Crimaldi

Cirillo

et al. 2021

Chem. Biol. Technol. Agric.

135

View full text Add to dashboard Cite

Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts.

show abstract

“…Tang et al [90] applied CNN on Ipomoea cairica L. sweets and compared with artificial intelligence, including LeNet, GoogleNet, AlexNet, VGG, and ResNet. A deep learning-based method for estimating the crop and weed distribution from images captured by a UAV leverages the CNN to perform image semantic segmentation and a post-processing step being applied to compute the weed [91]. LeNet, which is based on the CNN methodology, emerged as a promising technique because it used spatial information from UAV images inside that learning framework's architecture.…”

Section: Algorithms and Classification Techniques For Weed Mappingmentioning

confidence: 99%

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

et al. 2021

View full text Add to dashboard Cite

Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

show abstract

UAV Image Based Crop and Weed Distribution Estimation on Embedded GPU Boards

Cited by 20 publications

References 14 publications

Combined UAV‐ and tractor‐based stripe rust monitoring in winter wheat under field conditions

Combined UAV‐ and tractor‐based stripe rust monitoring in winter wheat under field conditions

Drone and sensor technology for sustainable weed management: a review

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

Contact Info

Product

Resources

About