ArticleApplications in plant sciences2020
Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 121 citations in OpenAlex.
- A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation.Scientific data · 2026Article
- Deep learning classification of INSV-associated weeds in Monterey county using a curated RGB image dataset.Scientific reports · 2025Article
- Enhancing Instance Segmentation in Agriculture: An Optimized YOLOv8 Solution.Sensors (Basel, Switzerland) · 2025Article
- Non-Destructive Monitoring of External Quality of Date Palm Fruit (Sensors (Basel, Switzerland) · 2024Article
- Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations.Sensors (Basel, Switzerland) · 2024Article
- Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision.Scientific data · 2024Article
- Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation.Nature communications · 2023Article
- Identifying and mapping individual medicinal plant Lamiophlomis rotata at high elevations by using unmanned aerial vehicles and deep learning.Plant methods · 2023Article
- WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion.Frontiers in plant science · 2023Article
- Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.Frontiers in plant science · 2023Review
- Multi-modal and multi-view image dataset for weeds detection in wheat field.Frontiers in plant science · 2022Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Review of Weed Detection Methods Based on Computer Vision.Sensors (Basel, Switzerland) · 2021Review
- Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots.Applications in plant sciences · 2020Article
- A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.Applications in plant sciences · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseWeed removal in agriculture is typically achieved using herbicides. The use of autonomous robots to reduce weeds is a promising alternative solution, although their implementation requires the precise detection and identification of crops and weeds to allow an efficient action.
methodsWe trained and evaluated an instance segmentation convolutional neural network aimed at segmenting and identifying each plant specimen visible in images produced by agricultural robots. The resulting data set comprised field images on which the outlines of 2489 specimens from two crop species and four weed species were manually drawn. We adjusted the hyperparameters of a mask region-based convolutional neural network (R-CNN) to this specific task and evaluated the resulting trained model.
resultsThe probability of detection using the model was quite good but varied significantly depending on the species and size of the plants. In practice, between 10% and 60% of weeds could be removed without too high of a risk of confusion with crop plants. Furthermore, we show that the segmentation of each plant enabled the determination of precise action points such as the barycenter of the plant surface. DISCUSSION: Instance segmentation opens many possibilities for optimized weed removal actions. Weed electrification, for instance, could benefit from the targeted adjustment of the voltage, frequency, and location of the electrode to the plant. The results of this work will enable the evaluation of this type of weeding approach in the coming months.
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