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Precision Weed Mapping and Management Based on Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 27181

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Special Issue Editors

Dr. Dionisio Andújar

E-Mail Website
Guest Editor

Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
Interests: robotics; artificial perception; plant-crop monitoring; agricultural machinery
Special Issues, Collections and Topics in MDPI journals

Dr. Jorge Martínez-Guanter

E-Mail Website
Guest Editor

Department of Aerospace Engineering and Fluid Mechanics Agroforestry Engineering Area, University of Seville, Ctra. Sevilla-Utrera km.1, 41013 Seville, Spain
Interests: UAV imagery; ML for remote sensing; computer vision; crop protection strategies; AI-based weed mapping; satellite crop monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the effort to increase crop yields while reducing and optimizing inputs, precision agriculture brings a broad range of potential improvements in prototypical and precommercial solutions. Differential management at optimum time and place ensures the efficiency and economic return as well as sustainability of agricultural work. However, the term precision agriculture also involves pest minimization, control of unwanted species, and generation of strategies for dealing with weeds. Continuous improvement in weed control operations is a permanent requirement for the development of agricultural activity. Due to the loss in productive potential and quality that they cause in the crops, the differential management of these weeds is fundamental in a context the search for sustainability and efficiency. Recent advances in this field are based on the combination of remote sensing with the use of cutting-edge technologies such as deep learning, computer vision, UAV robotics, multisensor systems, etc.

Remote sensing plays a key role in providing accurate spatial and temporal information. By collecting and analyzing data on different scales and resolutions, emergency models, identification patterns ,and site mapping can be generated to enable the design of advanced crop protection strategies. The latest advances in remote sensing using very high-resolution satellites are allowing the characterization and diagnosis of crop conditions based on reflectance data through visible, multispectral or hyperspectral sensing.

Aerial data collection has undergone a considerable change with the growth of UAVs, which have given birth to new, powerful sensor-bearing platforms for various agricultural applications. The growing adoption of these aerial platforms by producers, both large and small, is gradually taking place. It involves the integration of cost-effective technologies, adapted to existing field conditions, easy to use and with standardized components. UAV platforms can be assessed as promoters of precise weed control considering agricultural semistructured environments.

On the other hand, ground-based platforms and systems allow for high-quality details on crops and pests, which can be of great applicability in the detection, identification, and control of weeds. A variety of image-reflectance sensors, depth-cameras or optical distance sensors can be used in terrestrial platforms to obtain accurate weed pressure levels, presence or pre-emergence models for specific control. Scalability, measurement repeatability, and robustness in field conditions, among others, are key aspects in its deployment. Innovation in sensors, advancements in spectral image processing algorithms, and the use of AI-based systems are fundamental to the evolution and widespread adoption of precision farming techniques.

Advanced weed-control systems (both chemical or mechanical) rely on an accurate detection of weeds and reliable discrimination between weeds and crop plants. Spatial distribution, severity of the infestation or herbicide resistance degree are considered key parameters for characterizing a weed infestation scenario. Thus, the detection system is intended to collect information on target areas and make spatially selective weed-control decisions. A number of technologies facilitating this selective control, such as the use of GNSS precision guidance systems with differential corrections, computer vision, object-based image analysis, AI-based cognitive systems or high throughput phenotyping and 3D characterization, are based on remote sensing in order to perform correct discrimination between crops and weeds. Differentiation under field conditions becomes difficult because highly variable natural objects must be discriminated from a background with its own highly random characteristics. A variety of visual characteristics can be used for plant species identification, such as morphology, spectral reflectance, and visual texture. Dynamic light conditions, daily temperature variation, machine vibrations or a dusty environment are additional barriers that make automation in agriculture particularly challenging.

Constant monitoring of ground-level spot problems caused by weed needs advanced decision support systems. These key elements in the workflow allow the management of the information generated by sensors and to carry out the tasks of weed control effectively. In addition to agronomic aspects, these systems must integrate financial and accounting information, which allows detailed knowledge of economic aspects of performance and benefits of selective weed control.

This Special Issue will include cutting-edge research on the development and implementation of new support systems for precision agriculture and crop management. Papers are requested that address the latest developments for a wide range of tasks related to precision agriculture, including research and recent advances in the following areas related to crop management, with a focus on weed detection and related topics:

Advancements in optical sensors (RGB, multispectral, hyperspectral, etc..) for weed detection;
Deep learning in remote sensing for weed recognition and control methods;
Weed plant phenotyping;
Weed mapping and management;
Satellite imagery patterns for weed pressure modeling;
Yield estimation and losses associated to weed control;
3D modeling of weed species;
Multisensor systems: data fusion and integration in pests control;
Robotic applications: UAVs and ground platforms for weed control;
Autonomous systems for weed/pest control;
UAV and UGV communication;
Remote sensing and low energy consumption on precision crop management;
Decision support systems for crop management;
Economical aspect of site-specific management.

Dr. Dionisio Andújar
Dr. Jorge Martínez-Guanter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Weed control
UAV
Satellite
Robotics
Algorithms
Plant modeling
Phenotyping
Deep learning

Published Papers (6 papers)

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Editorial

Jump to: Research

4 pages, 190 KiB

Open AccessEditorial

An Overview of Precision Weed Mapping and Management Based on Remote Sensing

by Dionisio Andujar and Jorge Martinez-Guanter

Remote Sens. 2022, 14(15), 3621; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153621 - 28 Jul 2022

Cited by 3 | Viewed by 1665

Abstract

Precision Agriculture face the challenge of feeding an increasing population, maximizing yield while optimizing inputs [...] Full article

(This article belongs to the Special Issue Precision Weed Mapping and Management Based on Remote Sensing)

Research

Jump to: Editorial

29 pages, 13146 KiB

Open AccessArticle

Two-Stream Dense Feature Fusion Network Based on RGB-D Data for the Real-Time Prediction of Weed Aboveground Fresh Weight in a Field Environment

by Longzhe Quan, Hengda Li, Hailong Li, Wei Jiang, Zhaoxia Lou and Liqing Chen

Remote Sens. 2021, 13(12), 2288; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122288 - 11 Jun 2021

Cited by 16 | Viewed by 2821

Abstract

The aboveground fresh weight of weeds is an important indicator that reflects their biomass and physiological activity and directly affects the criteria for determining the amount of herbicides to apply. In precision agriculture, the development of models that can accurately locate weeds and predict their fresh weight can provide visual support for accurate, variable herbicide application in real time. In this work, we develop a two-stream dense feature fusion convolutional network model based on RGB-D data for the real-time prediction of the fresh weight of weeds. A data collection method is developed for the compilation and production of RGB-D data sets. The acquired images undergo data enhancement, and a depth transformation data enhancement method suitable for depth data is proposed. The main idea behind the approach in this study is to use the YOLO-V4 model to locate weeds and use the two-stream dense feature fusion network to predict their aboveground fresh weight. In the two-stream dense feature fusion network, DenseNet and NiN methods are used to construct a Dense-NiN-Block structure for deep feature extraction and fusion. The Dense-NiN-Block module was embedded in five convolutional neural networks for comparison, and the best results were achieved with DenseNet201. The test results show that the predictive ability of the convolutional network using RGB-D as the input is better than that of the network using RGB as the input without the Dense-NiN-Block module. The mAP of the proposed network is 75.34% (IoU value of 0.5), the IoU is 86.36%, the detection speed of the fastest model with a RTX2080Ti NVIDIA graphics card is 17.8 fps, and the average relative error is approximately 4%. The model proposed in this paper can provide visual technical support for precise, variable herbicide application. The model can also provide a reference method for the non-destructive prediction of crop fresh weight in the field and can contribute to crop breeding and genetic improvement. Full article

(This article belongs to the Special Issue Precision Weed Mapping and Management Based on Remote Sensing)

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19 pages, 6180 KiB

Open AccessArticle

Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping

by Chengsong Hu, Bishwa B. Sapkota, J. Alex Thomasson and Muthukumar V. Bagavathiannan

Remote Sens. 2021, 13(11), 2140; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112140 - 29 May 2021

Cited by 22 | Viewed by 3912

Abstract

Recent computer vision techniques based on convolutional neural networks (CNNs) are considered state-of-the-art tools in weed mapping. However, their performance has been shown to be sensitive to image quality degradation. Variation in lighting conditions adds another level of complexity to weed mapping. We focus on determining the influence of image quality and light consistency on the performance of CNNs in weed mapping by simulating the image formation pipeline. Faster Region-based CNN (R-CNN) and Mask R-CNN were used as CNN examples for object detection and instance segmentation, respectively, while semantic segmentation was represented by Deeplab-v3. The degradations simulated in this study included resolution reduction, overexposure, Gaussian blur, motion blur, and noise. The results showed that the CNN performance was most impacted by resolution, regardless of plant size. When the training and testing images had the same quality, Faster R-CNN and Mask R-CNN were moderately tolerant to low levels of overexposure, Gaussian blur, motion blur, and noise. Deeplab-v3, on the other hand, tolerated overexposure, motion blur, and noise at all tested levels. In most cases, quality inconsistency between the training and testing images reduced CNN performance. However, CNN models trained on low-quality images were more tolerant against quality inconsistency than those trained by high-quality images. Light inconsistency also reduced CNN performance. Increasing the diversity of lighting conditions in the training images may alleviate the performance reduction but does not provide the same benefit from the number increase of images with the same lighting condition. These results provide insights into the impact of image quality and light consistency on CNN performance. The quality threshold established in this study can be used to guide the selection of camera parameters in future weed mapping applications. Full article

(This article belongs to the Special Issue Precision Weed Mapping and Management Based on Remote Sensing)

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21 pages, 7216 KiB

Open AccessArticle

Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

by Pietro Mattivi, Salvatore Eugenio Pappalardo, Nebojša Nikolić, Luca Mandolesi, Antonio Persichetti, Massimo De Marchi and Roberta Masin

Remote Sens. 2021, 13(10), 1869; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101869 - 11 May 2021

Cited by 22 | Viewed by 4780

Abstract

Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands. Full article

(This article belongs to the Special Issue Precision Weed Mapping and Management Based on Remote Sensing)

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19 pages, 10556 KiB

Open AccessArticle

A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net

by Kunlin Zou, Xin Chen, Fan Zhang, Hang Zhou and Chunlong Zhang

Remote Sens. 2021, 13(2), 310; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020310 - 18 Jan 2021

Cited by 36 | Viewed by 5025

Abstract

Weeds are one of the main factors affecting the yield and quality of agricultural products. Accurate evaluation of weed density is of great significance for field management, especially precision weeding. In this paper, a weed density calculating and mapping method in the field is proposed. An unmanned aerial vehicle (UAV) was used to capture field images. The excess green minus excess red index, combined with the minimum error threshold segmentation method, was used to segment green plants and bare land. A modified U-net was used to segment crops from images. After removing the bare land and crops from the field, images of weeds were obtained. The weed density was evaluated by the ratio of weed area to total area on the segmented image. The accuracy of the green plant segmentation was 93.5%. In terms of crop segmentation, the intersection over union (IoU) was 93.40%, and the segmentation time of a single image was 35.90 ms. Finally, the determination coefficient of the UAV evaluated weed density and the manually observed weed density was 0.94, and the root mean square error was 0.03. With the proposed method, the weed density of a field can be effectively evaluated from UAV images, hence providing critical information for precision weeding. Full article

(This article belongs to the Special Issue Precision Weed Mapping and Management Based on Remote Sensing)

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Graphical abstract

22 pages, 2031 KiB

Open AccessFeature PaperArticle

Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

by Gerassimos G. Peteinatos, Philipp Reichel, Jeremy Karouta, Dionisio Andújar and Roland Gerhards

Remote Sens. 2020, 12(24), 4185; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244185 - 21 Dec 2020

Cited by 44 | Viewed by 5031

Abstract

The increasing public concern about food security and the stricter rules applied worldwide concerning herbicide use in the agri-food chain, reduce consumer acceptance of chemical plant protection. Site-Specific Weed Management can be achieved by applying a treatment only on the weed patches. Crop plants and weeds identification is a necessary component for various aspects of precision farming in order to perform on the spot herbicide spraying or robotic weeding and precision mechanical weed control. During the last years, a lot of different methods have been proposed, yet more improvements need to be made on this problem, concerning speed, robustness, and accuracy of the algorithms and the recognition systems. Digital cameras and Artificial Neural Networks (ANNs) have been rapidly developed in the past few years, providing new methods and tools also in agriculture and weed management. In the current work, images gathered by an RGB camera of Zea mays, Helianthus annuus, Solanum tuberosum, Alopecurus myosuroides, Amaranthus retroflexus, Avena fatua, Chenopodium album, Lamium purpureum, Matricaria chamomila, Setaria spp., Solanum nigrum and Stellaria media were provided to train Convolutional Neural Networks (CNNs). Three different CNNs, namely VGG16, ResNet–50, and Xception, were adapted and trained on a pool of 93,000 images. The training images consisted of images with plant material with only one species per image. A Top-1 accuracy between 77% and 98% was obtained in plant detection and weed species discrimination, on the testing of the images. Full article

(This article belongs to the Special Issue Precision Weed Mapping and Management Based on Remote Sensing)

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