Assessing the Capability and Potential of LiDAR for Weed Detection

Shahbazi, None Nooshin; Ashworth, Michael B.; Callow, Nik; Mian, Ajmal; Beckie, Hugh J.; Speidel, Stuart; Nicholls, Elliot; Flower, Ken

doi:10.3390/s21072328

Cited by 11 publications

(4 citation statements)

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“…This disparity in height serves as a key factor in effectively distinguishing between the weeds and the crop in maize fields. Examining the potential of Light Detection and Ranging (LiDAR) sensors for weed detection, the research emphasizes key parameters such as target size and orientation, elucidated through trials with artificial targets (Shahbazi et al, 2021) [97] . The findings underscore the direct influence of target size and orientation on detectability at varying scanning distances.…”

Section: Laser and 3d Lidar Based Weed Detection And Controlmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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Sensor-based weed detection is a significant breakthrough in precise weed management that reduces the need for manual work and herbicides. GPS systems have shown promising results, with a single pass covering 30-49% of the intra-row area. Additionally, using an RGB sensor and a laser sensor together can identify the center of a plant, regardless of lighting conditions. Robots have demonstrated the ability to distinguish 99.7% of crop plants in dense outdoor areas with high weed density. These advancements offer opportunities for more effective and environmentally-friendly weed management in different farming systems.

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Section: Laser and 3d Lidar Based Weed Detection And Controlmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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“…As an additional source in some studies, 3D data are used as lidar-point clouds or a rasterized point cloud in the form of a CHM [14,15]. However, particularly UAV-based studies using canopy models are very limited and are not mentioned in reviews concerning weed detection [4,5].…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Perennial Weed Detection in a Barley Field

2023

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In the field of precision agriculture, weed detection models combined with selective spraying by ground or aerial robotics are seen as promising approaches for increasing yield harvests while simultaneously minimizing herbicide use. The available labeled training data are a major bottleneck for developing and applying supervised deep learning models, and more automated methods for labeled data generation are therefore needed. Our research aims to address this need by introducing a rule-based method for label data generation for perennial weeds. For this research, a dataset of a barley field was collected using an unmanned aerial vehicle (UAV) with a flight altitude of 10 m. A multispectral and a thermal camera were used for the data collection. The preprocessed dataset consists of multispectral and thermal orthomosaic images along with a canopy height model. The methodological part of this article introduces a proposed rule-based method for labeled data generation for perennial weeds based on the Normalized Difference Vegetation Index (NDVI), and this approach is further used to generate labels for the measured data. The preprocessed data combined with the generated labels was used to train U-net models. Three data combinations are used for the training and testing: multispectral, multispectral–thermal and multispectral–thermal–canopy–height–model. This approach was used to evaluate whether additional data improve model performance. To evaluate the models on ground truth labels, they are tested on a manually annotated test dataset, which consists of 10% of the whole dataset. The tested models achieved an F1 score of 0.82–0.83 on the test dataset. This finding demonstrates that the rule-based labeling method generates valid labels for the perennial weed detection task. Furthermore, our study shows that data fusion improved the model slightly. The data combination of multispectral–thermal–canopy–height–model as input resulted in the best-performing model, with an F1 score of 0.835.

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“…Since then, significant progress has been made by using the results in many sectors, among which agriculture stands out, where it has been applied to the recognition of plants with significant results. In the last decade, some works have implemented the identification of plants and their position, relying on the plants' outline characteristics [59]. CNNs have also been used for soybean image recognition [60].…”

Section: Crop Identificationmentioning

confidence: 99%

Crop Identification and Growth Stage Determination for Autonomous Navigation of Agricultural Robots

Cortinas,

Emmi,

Gonzalez-de-Santos

2023

Agronomy

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This study introduces two methods for crop identification and growth stage determination, focused primarily on enabling mobile robot navigation. These methods include a two-phase approach involving separate models for crop and growth stage identification and a one-phase method employing a single model capable of handling all crops and growth stages. The methods were validated with maize and sugar beet field images, demonstrating the effectiveness of both approaches. The one-phase approach proved to be advantageous for scenarios with a limited variety of crops, allowing, with a single model, to recognize both the type and growth state of the crop and showed an overall Mean Average Precision (mAP) of about 67.50%. Moreover, the two-phase method recognized the crop type first, achieving an overall mAP of about 74.2%, with maize detection performing exceptionally well at 77.6%. However, when it came to identifying the specific maize growth state, the mAP was only able to reach 61.3% due to some difficulties arising when accurately categorizing maize growth stages with six and eight leaves. On the other hand, the two-phase approach has been proven to be more flexible and scalable, making it a better choice for systems accommodating a wide range of crops.

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Assessing the Capability and Potential of LiDAR for Weed Detection

Cited by 11 publications

References 50 publications

Advancements in precision control in intra-row weeding: A comprehensive review

Advancements in precision control in intra-row weeding: A comprehensive review

Weakly Supervised Perennial Weed Detection in a Barley Field

Crop Identification and Growth Stage Determination for Autonomous Navigation of Agricultural Robots

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