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1. 发明申请

US20220121887A1 DETECTION OF PLANT DISEASES WITH MULTI-STAGE, MULTI-SCALE DEEP LEARNING 有权
公开(公告)号：US20220121887A1
公开(公告)日：2022-04-21
申请号：US17567635
申请日：2022-01-03
申请人： CLIMATE LLC
发明人： Wei Guan , Yichuan Gui
IPC分类号： G06K9/62 , G06N3/08 , G06N3/04 , G06T7/00 , G06T3/40
摘要： In some embodiments, a computer-implemented method is disclosed. The method comprises receiving a plant image from a user device and applying a first digital model to first regions within the image for classifying each of the first regions into a class of a first set of classes corresponding to a first plurality of plant diseases, a healthy condition, or a combination of a second plurality of plant diseases. The method also includes applying a second digital model to one or more second regions within the image for classifying each of the one or more second regions into a class of a second set of classes corresponding to the second plurality of plant diseases. The method then includes transmitting classification data related to the classes of the first set of classes and the classes of the second set of classes to the user device into which the regions are classified.

2. 发明授权

US11380092B2 Ponding water detection on satellite imagery 有权
公开(公告)号：US11380092B2
公开(公告)日：2022-07-05
申请号：US17174164
申请日：2021-02-11
申请人： CLIMATE LLC
发明人： Wei Guan , Pramithus Khadka
IPC分类号： G06V20/13 , G06V20/10 , G06T7/00 , G06T3/00
摘要： A system for identifying ponding water located on a field from image data is described. In an approach, an image of an agricultural field is analyzed using a classifier that has been trained based on the spectral bands of labeled image pixels to identify a probability for each pixel within the image that the pixel corresponds to water. A flow simulation is performed to determine regions of the field that are likely to pool water after rainfall based on precipitation data, elevation data, and soil property data of the field. A graph of vertices representing the pixels and edges representing connections between neighboring pixels is generated. The probability of each pixel within the graph being ponding water is set based on the probability pixel being water, the likelihood that water will pool in the area represented by the pixel, the probability of neighboring pixels being ponding water, and a cropland mask that identifies which pixels correspond to cropland. A class for each pixel is then determined that maximizes the joint probability over the graph.

3. 发明授权

US11615276B2 Detection of plant diseases with multi-stage, multi-scale deep learning 有权
公开(公告)号：US11615276B2
公开(公告)日：2023-03-28
申请号：US17567635
申请日：2022-01-03
申请人： CLIMATE LLC
发明人： Wei Guan , Yichuan Gui
IPC分类号： G06V20/68 , G06K9/62 , G06N3/08 , G06N3/04 , G06T7/00 , G06T3/40 , G06V10/774 , G06V20/10 , A01G7/00 , G06V10/82
摘要： In some embodiments, a computer-implemented method is disclosed. The method comprises receiving a plant image from a user device and applying a first digital model to first regions within the image for classifying each of the first regions into a class of a first set of classes corresponding to a first plurality of plant diseases, a healthy condition, or a combination of a second plurality of plant diseases. The method also includes applying a second digital model to one or more second regions within the image for classifying each of the one or more second regions into a class of a second set of classes corresponding to the second plurality of plant diseases. The method then includes transmitting classification data related to the classes of the first set of classes and the classes of the second set of classes to the user device into which the regions are classified.

4. 发明授权

US11574465B2 In-season field level yield forecasting 有权
公开(公告)号：US11574465B2
公开(公告)日：2023-02-07
申请号：US16725869
申请日：2019-12-23
申请人： CLIMATE LLC
发明人： Yaqi Chen , Gardar Johannesson , Wei Guan
IPC分类号： G06V20/00 , G06V20/10 , G06Q50/02 , G06Q10/04 , G06N20/00
摘要： In an embodiment, digital images of agricultural fields are received at an agricultural intelligence processing system. Each digital image includes a set of pixels having pixel values, and each pixel value of a pixel includes a plurality of spectral band intensity values. Each spectral band intensity value describes a spectral band intensity of one band among several bands of electromagnetic radiation. For each of the agricultural fields, spectral band intensity values of each band are preprocessed at a field level using the digital images for that agricultural field resulting in preprocessed intensity values. The preprocessed intensity values are provided as input to a machine learning model. The model generates a predicted yield value for each field. The predicted yield value is used to update field yield maps of agricultural fields for forecasting and can be displayed via a graphical user interface (GUI) of a client computing device.

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