Patch Spraying and Rational Weed Mapping in Cereals

2015 ◽  
pp. 773-785
Author(s):  
S. Christensen ◽  
T. Heisel ◽  
J. V. Benlloch
Keyword(s):  
Weed Mapping ◽  
Patch Spraying

scholarly journals A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4245 ◽  
Author(s):  
Yanlei Xu ◽  
Zongmei Gao ◽  
Lav Khot ◽  
Xiaotian Meng ◽  
Qin Zhang
Keyword(s):  
Real Time ◽  
Field Experiments ◽  
Particle Swarm ◽  
Error Rates ◽  
Integrated System ◽  
Central Processor ◽  
Time Data ◽  
Weed Detection ◽  
Row Crops ◽  
Weed Mapping

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.

scholarly journals Weed Mapping in Vineyards Using RGB-D Perception

2021 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Dimitrios Kateris ◽  
Damianos Kalaitzidis ◽  
Vasileios Moysiadis ◽  
Aristotelis C. Tagarakis ◽  
Dionysis Bochtis
Keyword(s):  
Weed Management ◽  
Management Practices ◽  
Field Conditions ◽  
Yield Losses ◽  
Weed Mapping ◽  
Significant Yield ◽  
Different Levels

Weed management is one of the major challenges in viticulture, as long as weeds can cause significant yield losses and severe competition to the cultivations. In this direction, the development of an automated procedure for weed monitoring will provide useful data for understanding their management practices. In this work, a new image-based technique was developed in order to provide maps based on weeds’ height at the inter-row path of the vineyards. The developed algorithms were tested in many datasets from vineyards with different levels of weed development. The results show that the proposed technique gives promising results in various field conditions.

scholarly journals Impact of site-specific weed management on herbicide savings and winter wheat yield  

2013 ◽  
Vol 59 (No. 3) ◽  
pp. 101-107 ◽  
Author(s):  
P. Hamouz ◽  
K. Hamouzová ◽  
J. Holec ◽  
L. Tyšer
Keyword(s):  
Winter Wheat ◽  
Weed Management ◽  
Wheat Yield ◽  
The Other ◽  
Herbicide Application ◽  
Site Specific ◽  
Treatment Thresholds ◽  
Patch Spraying ◽  
Winter Wheat Yield ◽  
Weed Infestation

An aggregated distribution pattern of weed populations provides opportunity to reduce the herbicide application if site-specific weed management is adopted. This work is focused on the practical testing of site-specific weed management in a winter wheat and the optimisation of the control thresholds. Patch spraying was applied to an experimental field in Central Bohemia. Total numbers of 512 application cells were arranged into 16 blocks, which allowed the randomisation of four treatments in four replications. Treatment 1 represented blanket spraying and the other treatments differed by the herbicide application thresholds. The weed infestation was estimated immediately before the post-emergence herbicide application. Treatment maps for every weed group were created based on the weed abundance data and relevant treatment thresholds. The herbicides were applied using a sprayer equipped with boom section control. The herbicide savings were calculated for every treatment and the differences in the grain yield between the treatments were tested using the analysis of variance. The site-specific applications provided herbicide savings ranging from 15.6% to 100% according to the herbicide and application threshold used. The differences in yield between the treatments were not statistically significant (P = 0.81). Thus, the yield was not lowered by site-specific weed management.

scholarly journals Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles

2020 ◽  
Vol 10 (20) ◽  
pp. 7132 ◽  
Author(s):  
Jizhong Deng ◽  
Zhaoji Zhong ◽  
Huasheng Huang ◽  
Yubin Lan ◽  
Yuxing Han ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Real Time Image Processing ◽  
Weed Mapping ◽  
Real Time Image ◽  
Time Gap ◽  
Image Collection ◽  
Time Image

The timely and efficient generation of weed maps is essential for weed control tasks and precise spraying applications. Based on the general concept of site-specific weed management (SSWM), many researchers have used unmanned aerial vehicle (UAV) remote sensing technology to monitor weed distributions, which can provide decision support information for precision spraying. However, image processing is mainly conducted offline, as the time gap between image collection and spraying significantly limits the applications of SSWM. In this study, we conducted real-time image processing onboard a UAV to reduce the time gap between image collection and herbicide treatment. First, we established a hardware environment for real-time image processing that integrates map visualization, flight control, image collection, and real-time image processing onboard a UAV based on secondary development. Second, we exploited the proposed model design to develop a lightweight network architecture for weed mapping tasks. The proposed network architecture was evaluated and compared with mainstream semantic segmentation models. Results demonstrate that the proposed network outperform contemporary networks in terms of efficiency with competitive accuracy. We also conducted optimization during the inference process. Precision calibration was applied to both the desktop and embedded devices and the precision was reduced from FP32 to FP16. Experimental results demonstrate that this precision calibration further improves inference speed while maintaining reasonable accuracy. Our modified network architecture achieved an accuracy of 80.9% on the testing samples and its inference speed was 4.5 fps on a Jetson TX2 module (Nvidia Corporation, Santa Clara, CA, USA), which demonstrates its potential for practical agricultural monitoring and precise spraying applications.

Accuracy of a Global Positioning System (GPS) for Weed Mapping

1997 ◽  
Vol 11 (4) ◽  
pp. 782-786 ◽  
Author(s):  
Theodore M. Webster ◽  
John Cardina
Keyword(s):  
Measurement Error ◽  
Global Positioning System ◽  
Patch Size ◽  
Positioning System ◽  
Weed Mapping ◽  
Global Positioning ◽  
The Relationship

Experiments were conducted to test the accuracy of a global positioning system (GPS) in measuring the area of simulated weed patches of varying size and to determine the accuracy in navigating back to particular points in a field. Circular areas of 5, 50, and 500 m2 were established and measured using point and polygon features of a GPS. The GPS estimations of the area of those patches had errors ranging from 7 to 45%, 6 to 15%, and 3 to 6%, respectively, when compared to actual measurements. As patch size increased, errors decreased. A curve describing the relationship between GPS error and patch size had an excellent fit (r2 = 0.92). The error remained the same in all measurements across all patch sizes, but composed a smaller percentage of large patches. The GPS had submeter accuracy in navigation to the correct quadrat 73% of the time, located the correct quadrat 27% of the time, and invariably navigated to within 1.58 m of the correct quadrat. The relationship between patch size and measurement error was applied to natural infestations of hemp dogbane.

scholarly journals Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

2016 ◽  
Vol 47 ◽  
pp. 85-94 ◽  
Author(s):  
María Pérez-Ortiz ◽  
José Manuel Peña ◽  
Pedro Antonio Gutiérrez ◽  
Jorge Torres-Sánchez ◽  
César Hervás-Martínez ◽  
...  
Keyword(s):  
Weed Mapping
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