Abstract: 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.

Abstract: The embodiments of the present application provide a battery assembly, a battery module and a battery energy storage device.

Abstract: A battery energy storage module and a battery energy storage device. The battery energy storage module comprises: a structure body (101), a support member (102), and a cell group (103). A cavity for accommodating the cell group (103) is formed inside the structure body (101). The structure body (101) is provided with an opening. The cell group (103) is disposed in the cavity of the structure body (101) through the opening of the structure body (101). The support member (102) is used for supporting the structure body (101) to protect the cell group (103) disposed in the cavity of the structure body (101). Because the support member (102) is used for supporting the structure body (101) to protect the cell group (103) disposed in the cavity of the structure body (101), the battery energy storage module can have strong impact resistance, and can be applied to a full vehicle environment alone without being disposed in a protection tray.

Abstract: A system for preserving red meat by plasma active water (PAW) vacuum packaging includes a solution mixing tank, a plasma treatment chamber, an atomization spray module, a conveyor bell, a water circulation module, and a vacuum packaging module. The solution mixing tank, the plasma treatment chamber, the atomization spray module, and the water circulation module are sequentially connected through pipes. The atomization sprav module is configured to spray PAW onto the red meat on the conveyor belt, and the vacuum packaging module is configured to vacuum-package the red meat. Utilizing PAW improves the antibacterial ability of vacuum packaging while enhancing the color stability of red meat, greatly extending the shelf life of red meat, thereby preserving red meat in a green and economical way.

Abstract: In an embodiment, a computer-implemented method is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images and causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.

Abstract: In an embodiment, a computer-implemented method of detecting infected objects from large field-of-view images is disclosed. The method comprises receiving, by a processor, a digital image capturing multiple objects; generating, by the processor, a plurality of scaled images from the digital image respectfully corresponding to a plurality of scales; and computing a group of feature matrices for the digital image. The method further comprises, for each of the plurality of scaled images. selecting a list of candidate regions from the scaled image each likely to capture a single object; and for each of the list of candidate regions, performing the following steps: mapping the candidate region back to the digital image to obtain a mapped region; identifying a corresponding portion from each of the group of feature matrices based on the mapping; and determining whether the candidate region is likely to capture the single object infected with a disease based on the group of corresponding portions.

Abstract: 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.

Abstract: In an embodiment, a computer-implemented method is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images and causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.

Abstract: In an embodiment, a computer-implemented method of detecting infected objects from large field-of-view images is disclosed. The method comprises receiving, by a processor, a digital image capturing multiple objects; generating, by the processor, a plurality of scaled images from the digital image respectfully corresponding to a plurality of scales; and computing a group of feature matrices for the digital image. The method further comprises, for each of the plurality of scaled images. selecting a list of candidate regions from the scaled image each likely to capture a single object; and for each of the list of candidate regions, performing the following steps: mapping the candidate region back to the digital image to obtain a mapped region; identifying a corresponding portion from each of the group of feature matrices based on the mapping; and determining whether the candidate region is likely to capture the single object infected with a disease based on the group of corresponding portions.

Abstract: In an embodiment, a computer-implemented method is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images and causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.

Abstract: In an embodiment, a computer-implemented method of detecting infected objects from large field-of-view images is disclosed. The method comprises receiving, by a processor, a digital image capturing multiple objects; generating, by the processor, a plurality of scaled images from the digital image respectfully corresponding to a plurality of scales; and computing a group of feature matrices for the digital image. The method further comprises, for each of the plurality of scaled images. selecting a list of candidate regions from the scaled image each likely to capture a single object; and for each of the list of candidate regions, performing the following steps: mapping the candidate region back to the digital image to obtain a mapped region; identifying a corresponding portion from each of the group of feature matrices based on the mapping; and determining whether the candidate region is likely to capture the single object infected with a disease based on the group of corresponding portions.

Abstract: In an embodiment, a computer-implemented method of detecting infected objects from large field-of-view images is disclosed. The method comprises receiving, by a processor, a digital image capturing multiple objects; generating, by the processor, a plurality of scaled images from the digital image respectfully corresponding to a plurality of scales; and computing a group of feature matrices for the digital image. The method further comprises, for each of the plurality of scaled images. selecting a list of candidate regions from the scaled image each likely to capture a single object; and for each of the list of candidate regions, performing the following steps: mapping the candidate region back to the digital image to obtain a mapped region; identifying a corresponding portion from each of the group of feature matrices based on the mapping; and determining whether the candidate region is likely to capture the single object infected with a disease based on the group of corresponding portions.

Abstract: In an embodiment, a method of real-time disease recognition in a crop field is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images. The method further comprises causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.