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Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification

Plant Phenomics 2022年第1期4卷 409-418页

作者： Alexis Carlier Sebastien Dandrifosse Benjamin Dumont Benoit Mercatoris Biosystems Dynamics and Exchanges TERRA Teaching and Research CentreGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant Sciences TERRA Teaching and Research CentreGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium

The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different *** deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of ***,they remain complicated to implement and necessitate a huge training *** paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage.

关键词： Wheat heading maturity

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Non-Stomatal Processes are Responsible for the Decrease in Gross Primary Production of a Potato Crop During Edaphic Drought

SSRN

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SSRN 2023年

作者： Beauclaire, Quentin Heinesch, Bernard Longdoz, Bernard BIODYNE Biosystems Dynamics and Exchanges TERRA Teaching and Research Center Gembloux Agro-Bio Tech University of Liège Belgium

Soil water stress is one of the main limiting factors for agrosystem functioning, causing a reduction in gross primary production (GPP). However, it is still unclear whether GPP limitations originate from a direct stomatal response (SOL) or from other non-stomatal causes (NSOL). In this study, the effects of edaphic drought on a potato crop were investigated by using eddy covariance data from the Lonzée ICOS station (BE-Lon) in Belgium during four cropping periods (2006, 2010, 2014 and 2018). For the years when GPP anomalies were detected, the apparent maximum carboxylation rate (corresponding to a NSOL) started to decrease when the relative extractable water in the soil (REW) passed below a threshold corresponding to 76% of the maximum available water. At the same time, the slope parameter in the stomatal conductance optimality model remained constant or increased (absence of SOL). Consequently, the non-consideration of NSOL in the modeling of GPP led to an overestimation of GPP. At an hourly timescale, the carbon and water fluxes tended to decouple when soil water availability decreased, exhibiting a strict control of NSOL in hourly dynamics of GPP in opposition to transpiration. Our results show the need for better representation of the drought influence on photosynthesis processes by reducing photosynthetic capacities to properly model GPP and transpiration dynamics of croplands during dry episodes. © 2023, The Authors. All rights reserved.

关键词： Drought

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Characterisation of cereal morphology through proximal stereo vision under contrasting nitrogen inputs 12

Characterisation of cereal morphology through proximal stere...

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12th European Conference on Precision Agriculture, ECPA 2019

作者： Dandrifosse, S. Bouvry, A. Leemans, V. Dumont, B. Mercatoris, B. Biosystems Dynamics and Exchanges TERRA Teaching and Research Centre Gembloux Agro-Bio Tech Liège University Passage des Déportés 2 5030 Belgium Plant Sciences TERRA Teaching and Research Centre Gembloux Agro-Bio Tech Liège University Passage des Déportés 2 5030 Belgium

ISBN: (纸本)9789086863372

This study aimed to determine the capacity of a proximal stereo vision system to characterise the architecture of winter wheat and spring barley canopies. Images were acquired by a nadir pair of cameras, in two fields dedicated to fertilisation trials (Gembloux, Belgium). Soil and plants were separated through support vector machines. Dense 3D point clouds were computed, describing canopy height. The study of mean leaf angles and leaf angle distributions revealed different dynamics for wheat and barley but little effect of fertilisation. Due to its low cost and its compactness, such a 3D vision system is suitable for farm applications. © Wageningen Academic Publishers 2019

关键词： Morphology

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Influences of feeding behaviour and forage quality on diurnal methane emission dynamics of grazing cows 8

Influences of feeding behaviour and forage quality on diurna...

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8th European Conference on Precision Livestock Farming, 2017

作者： Blaise, Y. Andriamandroso, A.L.H. Heinesch, B. Beckers, Y. Castro Muñoz, E. Lebeau, F. Bindelle, J. TERRA Teaching and Research Center AgricultureIsLife Belgium AgroBioChem Precision Livestock and Nutrition Unit Belgium Biosystems Dynamics and Exchanges Gembloux Agro-Bio Tech University of Liege Passage des Déportés 2 Gembloux5030 Belgium Facultad de Ciencias Agrícolas Universidad Central del Ecuador Universitaria Quito170129 Ecuador

This study aimed to evaluate diurnal methane (CH4) emission dynamics of grazing cattle and highlight their relationships with biotic factors such as the feeding behaviour as well as seasonal changes in pasture characteristics. Existing methods to assess grazing ruminants’ daily CH4 emissions provide useful insights to investigate mitigation strategies relying on feeding and genetic selection. Nonetheless such methods based on tracer gases (SF6) or feeding bins equipped with sniffers (e.g. GreenFeed) can hardly cover diurnal CH4 emission fluctuations which can influence the accuracy of total CH4 production estimations. Previous studies in barns showed that emission dynamics strongly vary during post feeding time, leading to a possible bias in estimates of daily CH4 emissions as high as 100%. To investigate whether such fluctuations are also taking place on pasture, a portable device was designed with infrared CH4 and CO2 sensors measuring concentrations in the exhaled air at a high sampling rate (4 Hz). Six grazing dry red-pied cows were equipped with the device and motion sensors during runs of 24h to monitor CH4 and CO2 emissions and detect their feeding behaviours (grazing, rumination and other behaviours), respectively. This experiment was performed in summer and fall in order to cover seasonal changes in pasture forage quality. Methane emission was estimated from the CH4:CO2 concentration ratio and the metabolic CO2 production of the cows. As for barn studies, variations were observed in total daily CH4 emission due to the seasons and diurnal variations were also observed due to animal behaviours. Relationships between animal feeding behaviour and CH4 emissions patterns on pasture were also unravelled. © 2017 European Conference on Precision Livestock Farming. All rights reserved.

关键词： Mammals

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南疆沙尘区骏枣叶片水分含量检测的近红外光谱预处理方法对比（英文）

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光谱学与光谱分析 2019年第4期39卷 1323-1328页

作者：白铁成王涛陈佑启 MERCATORIS Beno■t TERRA Teaching and Research Centre Biosystems Dynamics and ExchangesGembloux Agro-Bio TechLiège University 塔里木大学信息工程学院新疆阿拉尔843300 中国农业科学院农业资源与农业区划研究所北京100081

南疆地区沙尘多、灰尘大,枣树叶片表面经常覆盖一定程度的粗颗粒度沙尘,为了有效去除沙尘、灰尘在枣树叶片水分光谱测量过程中产生的散射噪声和基线漂移,研究一种适用于风沙较大地区的枣树叶片水分含量的快速检测方法,以不同灌溉梯度下... 详细信息

南疆地区沙尘多、灰尘大,枣树叶片表面经常覆盖一定程度的粗颗粒度沙尘,为了有效去除沙尘、灰尘在枣树叶片水分光谱测量过程中产生的散射噪声和基线漂移,研究一种适用于风沙较大地区的枣树叶片水分含量的快速检测方法,以不同灌溉梯度下的枣树叶片为研究对象,通过近红外光谱仪获取120个叶片样本的1 000～1 800 nm的光谱数据,并同步测量叶片水分含量,采用归一化、移动窗口平滑、 SavitZky-Golay(SG)卷积平滑、 SG求导、标准正态变量校正(SNV)和多元散射校正(MSC)等方法对原始光谱进行预处理,分析对比不同方法对散射噪声的处理能力,采用偏最小二乘回归分析方法筛选了敏感波段和建立预测模型。实验结果表明,枣树叶片水分含量强吸收峰为1 443 nm,波谷为1 661 nm;归一化光谱并未消除1 000～1 400 nm波段的散射噪声;移动窗口平滑和SG卷积平滑并未改进光谱曲线,散射噪声仍然存在; SG导数光谱的光谱特征峰和特征谷明显左移,光谱曲线不够平滑,噪声明显; SNV和MSC方法具有较好的散射噪声消除能力。偏最小回归分析方法筛选特征波长的结果表明(设置筛选波长数量为5),基于原始光谱未筛选到1 443 nm的强波峰和1 661 nm的波谷附近的波段;基于归一化光谱在1 450 nm波峰附近筛选的波长有一定的偏差,在1 661 nm波谷附近的筛选的波长明显高于1 700 nm;基于移动窗口和SG卷积平滑光谱在1 443 nm具有一定的筛选能力,但并未筛选到1 661 nm附近的波长;导数光谱并未筛选到1 443和1 661 nm波段; SNV和MSC在波峰和波谷位置附近均筛选了敏感的光谱波段,其中MSC略优于SNV方法恰好在波峰和波谷位置,共筛选了1 002, 1 383, 1 411, 1 443和1 661 nm五个特征波段,也证明了MSC方法散射噪声和基线漂移处理能力最优,提高了敏感波长的筛选能力。偏最小二乘回归模型结果表明,不同预处理方法的RMSE值均较低, SNV和MSC方法改进了模型的预测结果,R^2高于0.7,其中基于MSC方法的模型具有最高的R^2和最低的RMSEP和RMSEPCV,R^2=0.750 4, RMSEP=0.034 3, RMSECV=0.021 5,预测结果较优。证明MSC方法对沙尘和颗粒度引入的散射噪声具有较好的去除能力,可改进波长的筛选、提高预测模型精度,为南疆沙尘区的枣树叶片水分含量的无损检测提供了有效方法。

关键词：叶片水分含量近红外光谱散射噪声预处理方法多元散射校正

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Local Unsupervised Wheat Head Segmentation

Local Unsupervised Wheat Head Segmentation

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IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Elias Ennadifi Sébastien Dandrifosse Mohammed El Amine Mokhtari Alexis Carlier Sohaib Laraba Benoît Mercatoris Bernard Gosselin Information Signal and Artificial Intelligence Lab Faculty of Engineering University of Mons Mons Belgium Biosystems Dynamics and Exchanges TERRA Teaching and Research Centre Gembloux Agro-Bio Tech University of Liège Liège Belgium

ISBN: (纸本)9781665464383

Traditional wheat head detection and segmentation methods based on machine learning algorithms suffer from issues such as low efficiency and poor accuracy, resulting in the algorithms’ inability to generalize. The recent advances in deep learning, specifically in object detection methods, as well as computer development, have enabled the development of robust wheat head detection and segmentation methods. However, while international datasets of box labels are available for head detection, mask labels for segmentation are missing, and collecting them on a large scale is prohibitively expensive, time-consuming, and difficult. In this paper, we propose an unsupervised approach for segmenting wheat heads based only on box labels. Multiple state-of-the-art object detection methods have been trained on reference datasets and our collected data in order to find the best model to extract head bounding boxes. The obtained boxes were used as input of an unsupervised segmentation model named DeepMAC, which predicts the head mask in each box. Then, those masks are exploited to train several state-of-the-art supervised segmentation models. These models showed promising results on the collected dataset, covering all the wheat development stages. The average F1 score of head bounding box detection is 0.93 and the average F1 score of segmentation is 0.86.

关键词： Deep learning Image segmentation Head Machine learning algorithms Computational modeling Object detection Predictive models

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Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization

Wheat Diseases Classification and Localization Using Convolu...

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作者： Ennadifi, Elias Laraba, Sohaib Vincke, Damien Mercatoris, Benoit Gosselin, Bernard Department of Information Signal and Artificial Intelligence Laboratory Polytech Mons University of Mons Mons Belgium Walloon Agricultural Research Centre Knowledge and Valorization of Agricultural Products Department Quality and Authentication of Agricultural Products Chaussee de Namur 24 Gembloux5030 Belgium Biosystems Dynamics and Exchanges Terra Teaching and Research Centre Gembloux Agro-Bio Tech University of Liege Passage des Deportes 2 Gembloux5030 Belgium

ISBN: (纸本)9781728180410

The world has been witnessing a population boom that has several implications including food security. Wheat is one of the world's most important crops in terms of production and consumption, and demand for it is increasing. On the other hand, diseases can damage the abundance and the quality of the crop, so this needs to be revealed through advanced methods. In recent years, along with the various technological developments, using Convolutional Neural Networks (CNN) has proved to be showing great results in many image classification tasks. However, deep learning models are generally considered as black boxes and it is difficult to understand what the model has learned. The purpose of this article is to detect diseases from wheat images using CNNs and to use visualization methods to understand what these models have learned. For this reason, a wheat database has been collected by CRA-W (Walloon Agricultural Research Center), which contains 1163 images and is classified into two groups namely sick and healthy. Moreover, we propose to use the mask R-CNN for segmentation and extraction of wheat spikes from the background. Furthermore, a visualization and interpretation method, namely Gradient-weighted Class Activation Mapping (GradCAM), is used to locate the disease on the wheat spikes in a non-supervised way. GradCAM is actually used generally to highlight the most important regions from the CNN model's viewpoint that are used to perform the classification. © 2020 IEEE.

关键词： Convolutional neural networks

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Cloud Platform using Big Data and HPC Technologies for Distributed and Parallels Treatments

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Procedia Computer Science 2018年 141卷 112-118页

作者： Olivier Debauche Sidi Ahmed Mahmoudi Saïd Mahmoudi Pierre Manneback Computer Science Unit Faculty of Engineering University of Mons 20 Place du Parc B-7000 Mons Belgium Biosystems Dynamics and Exchanges Axis Biosystem Engineering Department ULiège Gembloux Agro-Bio Tech University of Liège Passage des Déportés 2 B-5030 Gembloux Belgium

Smart agriculture is one of the most diverse research. In addition, the quantity of data to be stored and the choice of the most efficient algorithms to process are significant elements in this field. The storage of collecting data from Internet of Things (IoT), existing on distributed, local databases and open data need a particular infrastructure to federate all these data to make complex treatments. The storage of this wide range of data that comes at high frequency and variable throughput is particularly difficult. In this paper, we propose the use of distributed databases and high-performance computing architecture in order to exploit multiple re-configurable computing and application specific processing such as CPUs, GPUs, TPUs and FPGAs efficiently. This exploitation allows an accurate training for an application to machine learning, deep learning and unsupervised modeling algorithms. The last ones are used for training supervised algorithms on images when it labels a set of images and unsupervised algorithms on IoT data which are unlabeled with variable qualities. The processing of data is based on Hadoop 3.1 MapReduce to achieve parallel processing and use containerization technologies to distribute treatments on Multi GPU, MIC and FPGA. This architecture allows efficient treatments of data coming from several sources with a cloud high-performance heterogeneous architecture. The proposed 4 layers infrastructure can also implement FPGA and MIC which are now natively supported by recent version of Hadoop. Moreover, with the advent of new technologies like Intel® Movidius TM ; it is now possible to deploy CNN at the Fog level in the IoT network and to make inference with the cloud and therefore limit significantly the network traffic that result in reducing the move of large amounts of data to the cloud.

关键词： GPU FPGA MIC CPU TPU Cloud Big Data parallel distributed processing heterogeneous cloud architecture

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Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging 12

Characterisation of fungal diseases on winter wheat crop usi...

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12th European Conference on Precision Agriculture, ECPA 2019

作者： Bebronne, R. Michez, A. Leemans, V. Vermeulen, P. Dumont, B. Mercatoris, B. Biosystems Dynamics and Exchanges TERRA Teaching and Research Centre Gembloux Agro-Bio Tech Liège University Passage des Déportés 2 Gembloux5030 Belgium Forest Management TERRA Teaching and Research Centre Gembloux Agro-Bio Tech Liège University Passage des Déportés 2 Gembloux5030 Belgium Walloon Agricultural Research Centre Valorisation of Agricultural Products Department Food and Feed Quality Unit Chaussée de Namur 24 Gembloux5030 Belgium Plant Sciences TERRA Teaching and Research Centre Gembloux Agro-Bio Tech Liège University Passage des Déportés 2 Gembloux5030 Belgium

ISBN: (纸本)9789086863372

Winter wheat fungal diseases, responsible for high yield losses, can be assessed by computer vision to increase phenotyping performance. This study aims to compare multispectral imagery based on remote and proximal sensing for disease detection. Wavelength selection was achieved by ANOVA and stepwise regression. Prediction of disease severity was performed by means of an artificial neural network based on proximal sensing data. Septoria tritici blotch (STB) requires proximal measurements, but stripe and brown rusts can be detected from UAVs and from the ground. Prediction results obtained gave R2 of 0.55 and 0.57 for STB and stripe rust respectively. © Wageningen Academic Publishers 2019

关键词： Unmanned aerial vehicles (UAV)

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Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization

Wheat Diseases Classification and Localization Using Convolu...

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Intelligent Systems and Computer Vision (ISCV)

作者： Elias Ennadifi Sohaib Laraba Damien Vincke Benoît Mercatoris Bernard Gosselin Information Signal and Artificial Intelligence Laboratory Polytech Mons University of Mons Mons Belgium Walloon Agricultural Research Centre Quality and Authentication of Agricultural Products Chaussée de Namur 24 Gembloux Belgium Biosystems Dynamics and Exchanges TERRA Teaching and Research Centre Gembloux Agro-Bio Tech University of Liège Passage des Déportés 2 Gembloux Belgium

ISBN: (数字)9781728180410

ISBN: (纸本)9781728180427

关键词： Diseases Agriculture Task analysis Image segmentation Convolutional neural networks Machine learning Databases

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