Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review
Abstract
:1. Introduction
2. Overview of Herbicide Resistance Weed Management
2.1. Herbicide Resistance in Weeds (HR)
2.2. HR Weed Management Strategies
2.2.1. Traditional Weed Management Practices
2.2.2. Integrated Weed Management (IWM) and Site-Specific Weed Management (SSWM)
2.3. Challenges and Opportunities of Managing Herbicide Resistance Weed
2.3.1. Challenges of HR Weed Management
- i.
- Low adoption of resistance-avoidance tactics
- ii.
- Lack of herbicides with new mode of action
- iii.
- Lack of field-specific decision support system (DSS) for weed management
2.3.2. Opportunities for HR Weed Management
- i.
- Approach to herbicide discovery
- ii.
- HR crops
- iii.
- Address barriers to IWM
- iv.
- Advanced tools and technology: AI, remote sensing
- Remote Sensing with UAVs and satellites provides the opportunity to increase field scouting in a timely, efficient manner [54] but is not available for commercial agriculture due to its limited coverage and legal and regulatory issues. Detecting hotspots, maps created from remote sensing or piling, or both allow site-specific weed management of only the areas requiring corrective actions [54]. Remote sensing can provide accurate, site-specific data that can be converted into information used by decision support systems. Studies have shown that vegetation indices based on spectral reflectance captured by remote sensors can help determine herbicides’ efficacy and identify HR weeds. This information can help farmers in the selection and application timing of selected herbicides [52]. Martin et al. (2019) [55] investigated the impact of the height and speed of UAVs as well as different types of nozzles on spot sprayer uniformity and evaluated the effectiveness of the system economically [56].
- Robotics could integrate mechanical, cultural, and herbicidal tactics for timely weed management and at the same time, increase time, labor, and cost efficiency, flame weeding, radio wave, microwave energy, use of animals, AI for real-time image processing and decision making. Zhang et al., 2022 provided an overview of current robotic approaches, key technologies, current limitations, and potential research ideas for the future to manage weeds [57].
3. Overview of Machine Learning and Artificial Intelligence Applied for HR Weed Management
- Skills: the annotation of specific weed images can also require experts, as well as deploying machine learning technologies. On the contrary, FSL can facilitate annotators’ work since fewer images per class are needed to be labeled. Models that can easily learn new classes can also participate in the democratization of machine learning technologies by simplifying the optimization procedure, which otherwise can require robust computation architectures (e.g., GPUs) and complex hyperparameter searches. [70,71].
- I.
- Transfer learning
- II.
- Data augmentation
- III.
- Meta-learning
4. Weed Mapping and Classification
4.1. Reflectance Properties in Spatial and Spectral Weed Detection and Localization
4.2. Thermal Imaging for Susceptible vs. Resistance Weed Canopies
4.3. Spectral Measurements and Indices-Based Weed Detection
Index Name | Formula | Experimental Weed Population |
---|---|---|
Weed Spectral Resistance Index (WSRI) | Barnyard grass, Velvet leaf [125] | |
Spectral Weed Indices (SWI) | A, C, D are wavelengths chosen from the pool of eight selected wavelengths (A ≠ C ≠ D) and b is the weighting factor | Kochia AB = R460 nm; C = R520 nm; D = R760 nm Ragweed AB = R670 nm; C = R790 nm; D = R760 nm Water hemp AB = R760 nm; C = R490 nm; D = R790 nm [124] |
Red Edge Position (REP) | c1 and c2, intercepts and m1 and m2 represent the slopes of the far-red and near infra-red line | Grass species-Brachypodium genuense, Briza media, Bromus erectus and Festuca sp. Herb species–Anthemis carpatica, Cirsium creticum, Crepis pygmaea, Lamium garganicum, Onobrychis viciifolia, Tanacetum parthenium and Trifolium pretense [126] |
Ratio Vegetation Index (RVI) | R677 is wavelength at 677 nm and R710 is wavelength at 710 nm | Foxtail, Goosegrass, Round and lobed leaf pharbitis redroot amaranth, purslane, lambs quarters [127] |
Crofton Weed Index (CWI) | Crofton weed (Eupatorium adenophorum spreng) [128] | |
Normalized Crop sample Index (NCSI) | λ(i,j) = cell in the dataset representing sample i in spectral band j. λnormVecj = spectral reflectance of band j in the vector of a selected labeled rectangle from the crop population | Weeds of the genus Convoluaceae in Watermelon [129] |
4.4. Integrating Reflectance Properties with Machine Learning Techniques
Machine Learning Technique | Application | Working Principle | Special Features | References |
---|---|---|---|---|
Convolutional Neural Networks (CNN) | The deeper CNN is used to classify weeds and crops, whereas the shallow network is used to detect weeds. | Machine learning algorithm with convolutional layers to test the weed features using images taken by UAVs (such as the leaf shape and position) | Propose a low-cost weed Identification system to build the identification model with an accuracy of 92% | [132] |
Deep convolutional neural network (DCNN) | High-throughput phenotyping and accurate field management of resistant weeds | Spectral characteristics of susceptible weeds are different from resistant weeds after herbicide application | Capable of autonomously learning fundamental filters and combining them hierarchically | [125] |
Support vector machine (SVM) | Identification of weeds, predict herbicide resistance in weeds, determine the appropriate herbicide | Two-dimensional spatial map with an additional dimension of spectral information, allowing to collect (NDVI) | Reduce the influence of the unknown variability and effective in small-sample handling with an accuracy of 97% | [133] |
Artificial neural networks (ANNs) | Classify the weed species based on the color, texture, and leaf of weeds | Collect the spectral data in vegetation indices, canopy cover, and plant density by using remote sensors | ANN detects the weeds with an accuracy of up to 95% | [134] |
Random forest (RF) classifier | Real-time detection of the weed and crop for precision UAV spraying | UAV images are used in the combination of digital surface models (DSMs), then separate the weeds from the crops | A popular option for its generalized performance and operational speed with an accuracy of 96% | [135] |
k-nearest neighbors (KNN) | Evaluating crop damage from herbicides, Weed classification for real-time automatic sprayer | Based on the spectral data between crops and weeds | Classification and regression issues are addressed with an accuracy of 93%. | [136] |
ShuffleNet-v2 and VGGNet | Detecting and discriminating weeds susceptible to herbicide | Trained according to the herbicide weed control spectrum with the goal of autonomous spot-spraying herbicides. | High overall accuracy (≥0.999) | [137] |
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Plant Compound/Component | Spectral Range (nm) |
---|---|
Alpha-carotenoid | 420, 440 and 470 |
Beta-carotenoid | 425, 450 and 480 |
Chlorophyll a | 435, 670–680, and 740 |
Chlorophyll b | 480 and 650 |
Lutein | 425, 445, and 475 |
Anthocyanin | 400–550 |
Violaxanthin | 425, 450, and 475 |
Moisture | 970, 1450 and 1944 |
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Ghatrehsamani, S.; Jha, G.; Dutta, W.; Molaei, F.; Nazrul, F.; Fortin, M.; Bansal, S.; Debangshi, U.; Neupane, J. Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review. Sustainability 2023, 15, 1843. https://doi.org/10.3390/su15031843
Ghatrehsamani S, Jha G, Dutta W, Molaei F, Nazrul F, Fortin M, Bansal S, Debangshi U, Neupane J. Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review. Sustainability. 2023; 15(3):1843. https://doi.org/10.3390/su15031843
Chicago/Turabian StyleGhatrehsamani, Shirin, Gaurav Jha, Writuparna Dutta, Faezeh Molaei, Farshina Nazrul, Mathieu Fortin, Sangeeta Bansal, Udit Debangshi, and Jasmine Neupane. 2023. "Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review" Sustainability 15, no. 3: 1843. https://doi.org/10.3390/su15031843
APA StyleGhatrehsamani, S., Jha, G., Dutta, W., Molaei, F., Nazrul, F., Fortin, M., Bansal, S., Debangshi, U., & Neupane, J. (2023). Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review. Sustainability, 15(3), 1843. https://doi.org/10.3390/su15031843