Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots

Premise: Weed 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. Methods: We 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. Results: The 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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Main Authors: Champ, Julien, Mora-Fallas, Adán, Goeau, Hervé, Mata-Montero, Erick, Bonnet, Pierre, Joly, Alexis
Format: article biblioteca
Language:eng
Subjects:H60 - Mauvaises herbes et désherbage, N20 - Machines et matériels agricoles, U30 - Méthodes de recherche, détermination des espèces, automate, agriculture de précision, désherbage, mauvaise herbe, plante de culture, lutte culturale, désherbage mécanique, http://aims.fao.org/aos/agrovoc/c_10354, http://aims.fao.org/aos/agrovoc/c_25680, http://aims.fao.org/aos/agrovoc/c_92363, http://aims.fao.org/aos/agrovoc/c_8345, http://aims.fao.org/aos/agrovoc/c_8347, http://aims.fao.org/aos/agrovoc/c_1972, http://aims.fao.org/aos/agrovoc/c_2020, http://aims.fao.org/aos/agrovoc/c_8346,
Online Access:http://agritrop.cirad.fr/597011/
http://agritrop.cirad.fr/597011/1/aps3.11373.pdf
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spelling dig-cirad-fr-5970112024-01-29T03:10:55Z http://agritrop.cirad.fr/597011/ http://agritrop.cirad.fr/597011/ Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots. Champ Julien, Mora-Fallas Adán, Goeau Hervé, Mata-Montero Erick, Bonnet Pierre, Joly Alexis. 2020. Applications in Plant Sciences, 8 (7), n.spéc. Machine Learning in Plant Biology: From Genomics to Field Studies:e11373, 10 p.https://doi.org/10.1002/aps3.11373 <https://doi.org/10.1002/aps3.11373> Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots Champ, Julien Mora-Fallas, Adán Goeau, Hervé Mata-Montero, Erick Bonnet, Pierre Joly, Alexis eng 2020 Applications in Plant Sciences H60 - Mauvaises herbes et désherbage N20 - Machines et matériels agricoles U30 - Méthodes de recherche détermination des espèces automate agriculture de précision désherbage mauvaise herbe plante de culture lutte culturale désherbage mécanique http://aims.fao.org/aos/agrovoc/c_10354 http://aims.fao.org/aos/agrovoc/c_25680 http://aims.fao.org/aos/agrovoc/c_92363 http://aims.fao.org/aos/agrovoc/c_8345 http://aims.fao.org/aos/agrovoc/c_8347 http://aims.fao.org/aos/agrovoc/c_1972 http://aims.fao.org/aos/agrovoc/c_2020 http://aims.fao.org/aos/agrovoc/c_8346 Premise: Weed 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. Methods: We 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. Results: The 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/597011/1/aps3.11373.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1002/aps3.11373 10.1002/aps3.11373 info:eu-repo/semantics/altIdentifier/doi/10.1002/aps3.11373 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1002/aps3.11373 info:eu-repo/semantics/dataset/purl/https://doi.org/10.5281/zenodo.3906500
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic H60 - Mauvaises herbes et désherbage
N20 - Machines et matériels agricoles
U30 - Méthodes de recherche
détermination des espèces
automate
agriculture de précision
désherbage
mauvaise herbe
plante de culture
lutte culturale
désherbage mécanique
http://aims.fao.org/aos/agrovoc/c_10354
http://aims.fao.org/aos/agrovoc/c_25680
http://aims.fao.org/aos/agrovoc/c_92363
http://aims.fao.org/aos/agrovoc/c_8345
http://aims.fao.org/aos/agrovoc/c_8347
http://aims.fao.org/aos/agrovoc/c_1972
http://aims.fao.org/aos/agrovoc/c_2020
http://aims.fao.org/aos/agrovoc/c_8346
H60 - Mauvaises herbes et désherbage
N20 - Machines et matériels agricoles
U30 - Méthodes de recherche
détermination des espèces
automate
agriculture de précision
désherbage
mauvaise herbe
plante de culture
lutte culturale
désherbage mécanique
http://aims.fao.org/aos/agrovoc/c_10354
http://aims.fao.org/aos/agrovoc/c_25680
http://aims.fao.org/aos/agrovoc/c_92363
http://aims.fao.org/aos/agrovoc/c_8345
http://aims.fao.org/aos/agrovoc/c_8347
http://aims.fao.org/aos/agrovoc/c_1972
http://aims.fao.org/aos/agrovoc/c_2020
http://aims.fao.org/aos/agrovoc/c_8346
spellingShingle H60 - Mauvaises herbes et désherbage
N20 - Machines et matériels agricoles
U30 - Méthodes de recherche
détermination des espèces
automate
agriculture de précision
désherbage
mauvaise herbe
plante de culture
lutte culturale
désherbage mécanique
http://aims.fao.org/aos/agrovoc/c_10354
http://aims.fao.org/aos/agrovoc/c_25680
http://aims.fao.org/aos/agrovoc/c_92363
http://aims.fao.org/aos/agrovoc/c_8345
http://aims.fao.org/aos/agrovoc/c_8347
http://aims.fao.org/aos/agrovoc/c_1972
http://aims.fao.org/aos/agrovoc/c_2020
http://aims.fao.org/aos/agrovoc/c_8346
H60 - Mauvaises herbes et désherbage
N20 - Machines et matériels agricoles
U30 - Méthodes de recherche
détermination des espèces
automate
agriculture de précision
désherbage
mauvaise herbe
plante de culture
lutte culturale
désherbage mécanique
http://aims.fao.org/aos/agrovoc/c_10354
http://aims.fao.org/aos/agrovoc/c_25680
http://aims.fao.org/aos/agrovoc/c_92363
http://aims.fao.org/aos/agrovoc/c_8345
http://aims.fao.org/aos/agrovoc/c_8347
http://aims.fao.org/aos/agrovoc/c_1972
http://aims.fao.org/aos/agrovoc/c_2020
http://aims.fao.org/aos/agrovoc/c_8346
Champ, Julien
Mora-Fallas, Adán
Goeau, Hervé
Mata-Montero, Erick
Bonnet, Pierre
Joly, Alexis
Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
description Premise: Weed 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. Methods: We 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. Results: The 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.
format article
topic_facet H60 - Mauvaises herbes et désherbage
N20 - Machines et matériels agricoles
U30 - Méthodes de recherche
détermination des espèces
automate
agriculture de précision
désherbage
mauvaise herbe
plante de culture
lutte culturale
désherbage mécanique
http://aims.fao.org/aos/agrovoc/c_10354
http://aims.fao.org/aos/agrovoc/c_25680
http://aims.fao.org/aos/agrovoc/c_92363
http://aims.fao.org/aos/agrovoc/c_8345
http://aims.fao.org/aos/agrovoc/c_8347
http://aims.fao.org/aos/agrovoc/c_1972
http://aims.fao.org/aos/agrovoc/c_2020
http://aims.fao.org/aos/agrovoc/c_8346
author Champ, Julien
Mora-Fallas, Adán
Goeau, Hervé
Mata-Montero, Erick
Bonnet, Pierre
Joly, Alexis
author_facet Champ, Julien
Mora-Fallas, Adán
Goeau, Hervé
Mata-Montero, Erick
Bonnet, Pierre
Joly, Alexis
author_sort Champ, Julien
title Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
title_short Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
title_full Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
title_fullStr Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
title_full_unstemmed Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
title_sort instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
url http://agritrop.cirad.fr/597011/
http://agritrop.cirad.fr/597011/1/aps3.11373.pdf
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