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1. 发明申请

WO2021233698A1 SYSTEM AND METHOD FOR ESTIMATING VEGETATION COVERAGE IN A REAL-WORLD ENVIRONMENT 审中-公开
公开(公告)号：WO2021233698A1
公开(公告)日：2021-11-25
申请号：PCT/EP2021/062178
申请日：2021-05-07
申请人： BASF SE
发明人： PICON RUIZ, Artzai , GONZALEZ SAN EMETERIO, Miguel , BERECIARTUA-PEREZ, Aranzazu , GOMEZ ZAMANILLO, Laura , JIMENEZ RUIZ, Carlos Javier , ROMERO RODRIGUEZ, Javier , KLUKAS, Christian , EGGERS, Till , ECHAZARRA HUGUET, Jone , NAVARRA-MESTRE, Ramon
IPC分类号： G06K9/00 , G06K9/20 , G06K9/62
摘要： Computer-implemented method and system (100) for estimating vegetation coverage in a real-world environment. The system receives an RGB image (91) of a real-world scenery (1) with one or more plant elements (10) of one or more plant species. At least one channel of the RGB image (91) is provided to a semantic regression neural network (120) which is trained to estimate at least a near-infrared channel (NIR) from the RGB image. The system obtains an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network (120) to the at least one RGB channel (91). A multi-channel image (92) comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image and the estimated near-infrared channel (NIR), is provided as test input (TI1) to a semantic segmentation neural network (130) trained with multi-channel images to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements. The system segments the test input (TI1) using the semantic segmentation neural network (130) resulting in a vegetation coverage map (93) indicating pixels of the test input associated with plant elements (10) and indicating pixels of the test input not associated with plant elements.

2. 发明申请

WO2021185745A1 QUANTIFYING BIOTIC DAMAGE ON PLANT LEAVES, BY CONVOLUTIONAL NEURAL NETWORKS 审中-公开
公开(公告)号：WO2021185745A1
公开(公告)日：2021-09-23
申请号：PCT/EP2021/056503
申请日：2021-03-15
申请人： BASF SE
发明人： BERECIARTUA-PEREZ, Aranzazu , PICON RUIZ, Artzai , SPANGLER, Corinna Maria , KLUKAS, Christian , EGGERS, Till , ECHAZARRA HUGUET, Jone , NAVARRA-MESTRE, Ramon
IPC分类号： G06K9/34 , G06K9/46 , G06N3/04
摘要： To quantify biotic damage in leaves of crop plants, a computer receives (701A) a leaf-image taken from a particular crop plant. The leaf-image shows at least one of the leaves of the particular crop plant. Using a first convolutional neural network (CNN, 262), the computer processes the leaf-image to derive a segmented leaf-image (422) being a contiguous set of pixels that show a main leaf of the particular plant completely. The first CNN has been trained by a plurality of leaf-annotated leaf-images (601A), wherein the leaf-images are annotated to identify main leaves (461). Using a second CNN (272), the computer processes the single-leaf-image by regression to obtain a damage degree (432).

3. 发明申请

WO2021063929A1 QUANTIFYING OBJECTS ON PLANTS BY ESTIMATING THE NUMBER OF OBJECTS ON PLANT PARTS SUCH AS LEAVES, BY CONVOLUTIONAL NEURAL NETWORKS THAT PROVIDE DENSITY MAPS 审中-公开
公开(公告)号：WO2021063929A1
公开(公告)日：2021-04-08
申请号：PCT/EP2020/077197
申请日：2020-09-29
申请人： BASF SE
发明人： ALVAREZ GILA, Aitor , ORTIZ BARREDO, Amaia Maria , ROLDAN LOPEZ, David , ROMERO RODRIGUEZ, Javier , SPANGLER, Corinna Maria , KLUKAS, Christian , EGGERS, Till , ECHAZARRA HUGUET, Jone , NAVARRA MESTRE, Ramon , PICON RUIZ, Artzai , BERECIARTUA PEREZ, Aranzazu
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62
摘要： Quantifying plant infestation is performed by estimating the number of biological objects (132) on parts (122) of a plant (112). A computer (202) receives a plant-image (412) taken from a particular plant (112). The computer (202) uses a first convolutional neural network (262/272) to derive a part-image (422) that shows a part of the plant. The computer (202) splits the part-image into tiles and uses a second network to process the tiles to density maps. The computer (202) combines the density maps to a combined density map in the dimension of the part-image and integrates the pixel values to an estimate number of objects for the part. Object classes (132(1), 132(2)) can be differentiated to fine-tune the quantification to identify class-specific countermeasures.

4. 发明申请

WO2023021084A1 LINEAR MODEL VALIDATION 审中-公开
公开(公告)号：WO2023021084A1
公开(公告)日：2023-02-23
申请号：PCT/EP2022/072949
申请日：2022-08-17
申请人： BASF SE
发明人： PROCTOR, Iain , SZOECS, Eduard , KRITIKOS, Georgios , EGGERS, Till
IPC分类号： G06N3/08 , G06N20/00 , G06N3/04
摘要： The present invention relates to validating a linear model. Input data is received (102) and the linear model to be validated is provided (104). Predicted data is determined based on processing input data by the linear model (106). Residual data is determined based on a difference between the predicted data and the input data (108). A set of validation data including homoscedasticity validation data or normality validation data is generated based on the residual data (110). A binary classifier is provided and used for determining whether the set of validation data fulfills a validation condition (112), namely a homoscedasticity condition or a normality condition. The binary classifier is a trained data driven model that outputs that the validation condition is fulfilled or not fulfilled depending on the set of validation data. Finally, it is determined whether the linear model is valid based on the output of the binary classifier (114).

5. 发明申请

WO2022207888A2 ESTIMATING PROPERTIES OF PHYSICAL OBJECTS, BY PROCESSING IMAGE DATA WITH NEURAL NETWORKS 审中-公开
公开(公告)号：WO2022207888A2
公开(公告)日：2022-10-06
申请号：PCT/EP2022/058723
申请日：2022-03-31
申请人： BASF SE
发明人： TANEJA, Rahul , SIAL, Kamran , EGGERS, Till , KEUPER, Margret , NAVARRA-MESTRE, Ramon , FISCHER, Sebastian , SCHARNER, Mike , ROMERO RODRIGUEZ, Javier , POLO LOPEZ, Franciso Manuel , MARTIN PALMA, Andres
IPC分类号： G06V20/10 , A01G7/06 , G06T7/00 , G06V10/44 , G06N3/04 , G06V20/17 , A01G7/00 , G06T7/0004 , G06V10/454 , G06V20/188
摘要： The present disclosure relates to image processing or computer vision techniques. A computer-implemented method is provided for determining a damage status of a physical object, the method comprising the steps of receiving a surface image of the physical object; and providing a pre-trained machine learning model to derive property values from the received surface map, wherein each property value is indicative of a damage index at a respective location, wherein the property values are preferably usable for monitoring and/or controlling a production process of the physical object. In this way, it is possible to reliably identify local defects and ensure that it is accurate enough to apply the chemical products in suitable amounts.

6. 发明申请

WO2017194276A9 SYSTEM AND METHOD FOR DETECTING PLANT DISEASES 审中-公开
公开(公告)号：WO2017194276A9
公开(公告)日：2017-11-16
申请号：PCT/EP2017/059231
申请日：2017-04-19
申请人： BASF SE
发明人： ALEXANDER, Johannes , EGGERS, Till , PICON, Artzai , ALVAREZ-GILA, Aitor , ORTIZ BARREDO, Amaya Maria , DÍEZ-NAVAJAS, Ana María
IPC分类号： G06K9/00
摘要： A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11)of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1). A filtering module (140) configured: to identify one or more clusters (C1 to Cn) by one or more visual features within the extracted image portions (11e) wherein each cluster is associated with a plant element portion showing characteristics of a plant disease; and to filter one or more candidate regions from the identified one or more clusters (C1 to Cn) according to a predefined threshold, by using a Bayes classifier that models visual feature statistics which are always present on a diseased plant image. A plant disease diagnosis module (150) configured to extract, by using a statistical inference method, from each candidate region (C4, C5, C6, Cn) one or more visual features to determine for each candidate region one or more probabilities indicating a particular disease; and to compute a confidence score (CS1) for the particular disease by evaluating all determined probabilities of the candidate regions (C4, C5, C6, Cn).

7. 发明申请

WO2022101419A1 COMPUTER-IMPLEMENTED METHOD FOR PROVIDING TRAINING DATA FOR A MACHINE LEARNING ALGORITHM FOR CLASSIFYING PLANTS INFESTED WITH A PATHOGEN 审中-公开
公开(公告)号：WO2022101419A1
公开(公告)日：2022-05-19
申请号：PCT/EP2021/081542
申请日：2021-11-12
申请人： BASF SE
发明人： SIEPE, Isabella , BUSCH, Kristina , EGGERS, Till , NAVARRA-MESTRE, Ramon , HADEN, Egon , ARNHOLD, Jessica , FISCHER, Sebastian , MARTIN PALMA, Andres , KLUKAS, Christian , FRIEDEL, Swetlana , STUERMER-STEPHAN, Bastian , HAHN, Stefan , TRESCH, Stefan
IPC分类号： G06K9/00 , G06K9/62 , G06V10/82 , G06V20/69
摘要： Computer-implemented method for providing training data for a machine learning algorithm for image classifying a pathogen infestation of a plant, comprising the steps: providing image data of a plant or a plant part infested with a pathogen; providing genetic result data of the plant or the plant part to which the image data referred comprising at least information about the type of pathogen; labeling the image data with the genetic result data.

8. 发明申请

WO2021165512A2 QUANTIFYING PLANT INFESTATION BY ESTIMATING THE NUMBER OF BIOLOGICAL OBJECTS ON LEAVES, BY CONVOLUTIONAL NEURAL NETWORKS THAT USE TRAINING IMAGES OBTAINED BY A SEMI-SUPERVISED APPROACH 审中-公开
公开(公告)号：WO2021165512A2
公开(公告)日：2021-08-26
申请号：PCT/EP2021/054233
申请日：2021-02-19
申请人： BASF SE
发明人： BERECIARTUA PEREZ, Aranzazu , PICON RUIZ, Artzai , ALVAREZ GILA, Aitor , ECHAZARRA HUGUET, Jone , EGGERS, Till , KLUKAS, Christian , NAVARRA MESTRE, Ramon , GOMEZ ZAMANILLO, Laura
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62 , G06N3/02 , G06K9/00664 , G06K9/4628 , G06K9/4652 , G06K9/6272 , G06N20/10 , G06N3/0454 , G06N3/0481 , G06N3/082
摘要： A computer generates a training set with annotated images (473) to train a convolutional neural network (CNN). The computer receives leaf-images showing leaves and biological objects such as insects, in a first color-coding (413-A), changes the color-coding of the pixels to a second color-coding and thereby enhances the contrast (413-C), assigns pixels in the second color-coding to binary values (413-D), differentiates areas with contiguous pixels in the first binary value into non-insect areas and insect areas by an area size criterion (413-E), identifies pixel-coordinates of the insect areas with rectangular tile-areas (413-F), and annotates the leaf-images in the first color-coding by assigning the pixel-coordinates to corresponding tile-areas. The annotated image is then used to train the CNN for quantifying plant infestation by estimating the number of biological object such as insects on the leaves of plants.

9. 发明申请

WO2017194276A1 SYSTEM AND METHOD FOR DETECTING PLANT DISEASES 审中-公开
标题翻译：用于检测植物病害的系统和方法
公开(公告)号：WO2017194276A1
公开(公告)日：2017-11-16
申请号：PCT/EP2017/059231
申请日：2017-04-19
申请人： BASF SE
发明人： ALEXANDER, Johannes , EGGERS, Till , PICON, Artzai , ALVAREZ-GILA, Aitor , ORTIZ BARREDO, Amaya Maria , DÍEZ-NAVAJAS, Ana María
IPC分类号： G06K9/00
CPC分类号： G06K9/00
摘要： A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11)of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1). A filtering module (140) configured: to identify one or more clusters (C1 to Cn) by one or more visual features within the extracted image portions (11e) wherein each cluster is associated with a plant element portion showing characteristics of a plant disease; and to filter one or more candidate regions from the identified one or more clusters (C1 to Cn) according to a predefined threshold, by using a Bayes classifier that models visual feature statistics which are always present on a diseased plant image. A plant disease diagnosis module (150) configured to extract, by using a statistical inference method, from each candidate region (C4, C5, C6, Cn) one or more visual features to determine for each candidate region one or more probabilities indicating a particular disease; and to compute a confidence score (CS1) for the particular disease by evaluating all determined probabilities of the candidate regions (C4, C5, C6, Cn).
摘要翻译：用于确定植物疾病的系统（100），方法和计算机程序产品。该系统包括被配置为接收植物的图像（10）的接口模块（110），图像（10）包括至少一个植物元件（1）的视觉表示（11）。颜色标准化模块（120）被配置为将颜色恒常性方法应用于所接收的图像（10）以生成颜色标准化图像。提取器模块（130）被配置为从颜色标准化图像提取一个或多个图像部分（11e），其中提取的图像部分（11e）对应于至少一个植物元件（1）。过滤模块（140），其被配置为：通过所提取的图像部分（11e）内的一个或多个视觉特征来识别一个或多个聚类（C1至Cn），其中每个聚类与显示植物疾病特征的植物元件部分相关联; 并且通过使用对总是存在于患病植物图像上的视觉特征统计进行建模的贝叶斯分类器，根据预定义的阈值从所识别的一个或多个聚类（C1至Cn）中过滤一个或多个候选区域。一种植物病害诊断模块（150），其被配置为通过使用统计推断方法从每个候选区域（C4，C5，C6，Cn）提取一个或多个视觉特征以针对每个候选区域确定一个或多个指示特定疾病; 并通过评估候选区域（C4，C5，C6，Cn）的所有确定的概率来计算特定疾病的置信度评分（CS1）。

10. 发明申请

WO2022117772A1 SYSTEM AND METHOD FOR DETERMINING DAMAGE ON PLANTS AFTER HERBICIDE APPLICATION 审中-公开
公开(公告)号：WO2022117772A1
公开(公告)日：2022-06-09
申请号：PCT/EP2021/084072
申请日：2021-12-02
申请人： BASF SE
发明人： BERECIARTUA-PEREZ, Aranzazu , GOMEZ ZAMANILLO, Laura , PARRA RAPADO, Liliana , PICON RUIZ, Artzai , KLUKAS, Christian , EGGERS, Till , ECHAZARRA HUGUET, Jone , NAVARRA-MESTRE, Ramon
IPC分类号： G06K9/00 , G06K9/62 , G06N3/04
摘要： A computer (100) estimates the damage on plants caused by herbicide application. The computer uses a single regression deep learning model (RDLM1). An interface (110) receives a zenithal image (20-1) representing a real-world situation at an agricultural growth location (AGL1) with non-overlapping plants associated with a particular plant species identifier (ID). The plants have been subject to herbicide treatment, with the zenithal image being recorded a certain number of days after the treatment (DAT). An image preprocessor (120) segments the received image (20-1) into pixels associated with plant elements and into pixels associated with non-plant elements. The preprocessor thereby obtains a masked image (20- 1m). The regression deep learning model (RDLM1) receives a test input with the masked image (20-1m), the respective number of days after treatment (DAT), and the respective plant species identifier (ID1), and outputs a predicted phytotoxicity value (PDCU) for the test input. The deep learning model has been trained with plant images for multiple plant species and for multiple days-after-treatment-values and with multiple herbicides types and doses.

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