<i>UAV Image-Based Weed Detection in Grassland Toward Site-Specific Weed Control</i>

2019 ◽  
Author(s):  
Ryo Sugiura ◽  
Kazufumi Fujiwara
2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Guy Coleman ◽  
William Salter ◽  
Michael Walsh

AbstractThe use of a fallow phase is an important tool for maximizing crop yield potential in moisture limited agricultural environments, with a focus on removing weeds to optimize fallow efficiency. Repeated whole field herbicide treatments to control low-density weed populations is expensive and wasteful. Site-specific herbicide applications to low-density fallow weed populations is currently facilitated by proprietary, sensor-based spray booms. The use of image analysis for fallow weed detection is an opportunity to develop a system with potential for in-crop weed recognition. Here we present OpenWeedLocator (OWL), an open-source, low-cost and image-based device for fallow weed detection that improves accessibility to this technology for the weed control community. A comprehensive GitHub repository was developed, promoting community engagement with site-specific weed control methods. Validation of OWL as a low-cost tool was achieved using four, existing colour-based algorithms over seven fallow fields in New South Wales, Australia. The four algorithms were similarly effective in detecting weeds with average precision of 79% and recall of 52%. In individual transects up to 92% precision and 74% recall indicate the performance potential of OWL in fallow fields. OWL represents an opportunity to redefine the approach to weed detection by enabling community-driven technology development in agriculture.


Author(s):  
Nebojša Nikolić ◽  
Davide Rizzo ◽  
Elisa Marraccini ◽  
Alicia Ayerdi Gotor ◽  
Pietro Mattivi ◽  
...  

Highlights- Efficacy of UAVs and emergence predictive models for weed control has been confirmed. - Combination of time-specific and site-specific weed control provides optimal results.- Use of timely prescription maps can substantially reduce herbicide use.   Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.


2020 ◽  
Vol 34 (5) ◽  
pp. 704-710
Author(s):  
Michael J. Walsh ◽  
Caleb C. Squires ◽  
Guy R. Y. Coleman ◽  
Michael J. Widderick ◽  
Adam B. McKiernan ◽  
...  

AbstractAustralian conservation cropping systems are practiced on very large farms (approximately 3,000 ha) where herbicides are relied on for effective and timely weed control. In many fields, though, there are low weed densities (e.g., <1.0 plant 10 m−2) and whole-field herbicide treatments are wasteful. For fallow weed control, commercially available weed detection systems provide the opportunity for site-specific herbicide treatments, removing the need for whole-field treatment of fallow fields with low weed densities. Concern about the sustainability of herbicide-reliant weed management systems remain and there has not been interest in the use of weed detection systems for alternative weed control technologies, such as targeted tillage. In this paper, we discuss the use of a targeted tillage technique for site-specific weed control in large-scale crop production systems. Three small-scale prototypes were used for engineering and weed control efficacy testing across a range of species and growth stages. With confidence established in the design approach and a demonstrated 100% weed-control potential, a 6-m wide pre-commercial prototype, the “Weed Chipper,” was built incorporating commercially available weed-detection cameras for practical field-scale evaluation. This testing confirmed very high (90%) weed control efficacies and associated low levels (1.8%) of soil disturbance where the weed density was fewer than 1.0 plant 10 m−2 in a commercial fallow. These data established the suitability of this mechanical approach to weed control for conservation cropping systems. The development of targeted tillage for fallow weed control represents the introduction of site-specific, nonchemical weed control for conservation cropping systems.


2021 ◽  
Vol 64 (2) ◽  
pp. 557-563
Author(s):  
Piyush Pandey ◽  
Hemanth Narayan Dakshinamurthy ◽  
Sierra N. Young

HighlightsRecent research and development efforts center around developing smaller, portable robotic weeding systems.Deep learning methods have resulted in accurate, fast, and robust weed detection and identification.Additional key technologies under development include precision actuation and multi-vehicle planning. Keywords: Artificial intelligence, Automated systems, Automated weeding, Weed control.


2020 ◽  
Author(s):  
Saraswathi Shanmugam ◽  
Eduardo Assunção ◽  
Ricardo Mesquita ◽  
André Veiros ◽  
Pedro D. Gaspar

A weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control. Keywords: Detection, Weed, Agriculture 4.0, Computational vision, Robotics


2016 ◽  
Vol 122 ◽  
pp. 103-111 ◽  
Author(s):  
Jing-Lei Tang ◽  
Xiao-Qian Chen ◽  
Rong-Hui Miao ◽  
Dong Wang

Agriculture, although known as the backbone of the Indian economy, is facing crisisin terms of production. One of the major issues in the agriculture sector is the growth of weeds among the crops. They compete with the desired plants for various resources and hence their growth must be inhibited. At present weeds are removed either manually, which is a time consuming and labour intensive task, or herbicides are being sprayed uniformly all over the field to keep them under check. Spraying of herbicide is very inefficient as the chemical contributes less to weed control and cause contamination of the environment. The main objective of this work is a weed control system that differentiates the weed from crops and restricts weed growth alone by the precise removal of the weed. This is implemented by capturing the images of the field at regular intervals and processing them with a Raspberry Pi board by making use of an image processing algorithm to differentiate the desired plants from the weeds. This is based on various features like colour and size of the crop and weed. Once the weeds are identified and located correctly through image processing, a signal is transmitted from the Raspberry Pi board to turn on the weed cutting system. The selective activation of the weed removal system helps in the precise removal of the weeds and this provides a better environment for the desired plants to grow well.


1996 ◽  
Vol 18 (4) ◽  
pp. 523-535 ◽  
Author(s):  
Caleb A. Oriade ◽  
Robert P. King ◽  
Frank Forcella ◽  
Jeffrey L. Gunsolus

Weed Research ◽  
2009 ◽  
Vol 49 (3) ◽  
pp. 233-241 ◽  
Author(s):  
S CHRISTENSEN ◽  
H T SØGAARD ◽  
P KUDSK ◽  
M NØRREMARK ◽  
I LUND ◽  
...  

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