Abstract: A system and a method determine physical characteristics of plants positioned beneath respective spray units. The physical characteristics are determined by analyzing respective images of each plant using imaging devices. A first image of each plant is obtained using a respective low-spectral-resolution camera. A second image of at least one plant is obtained using a higher-spectral-resolution camera. A correlation coefficient or correlatable features are generated based on the spectral characteristics of the elements of the first image and the spectral characteristics of the elements of second image of the at least one plant. The correlation coefficient or correlatable features are applied to the elements of each first image of the other plants to produce a respective higher-spectral-resolution second image of each of the other plants. The spectral characteristics of the second images are analyzed to determine physical characteristics of the at least one plant and the other plants.

Abstract: A camera is mounted on the front of a sprayer. Targets are identified from images generated by the camera. The location of the target is identified relative to a nozzle on the sprayer. A control signal is generated to control the timing and duration of actuation of the nozzle.

Abstract: An agricultural material application machine includes a material reservoir, a controllable valve, a pump that pumps material from the material reservoir to the controllable valve, an image sensor that captures in image of a portion of the worksite ahead of the controllable valve, and a light sensor that detects ambient lighting conditions at the worksite and generates sensor data indicative of the ambient lighting conditions. The machine further includes one or more processors and memory storing instructions executable by the one or more processors. The instructions, when executed by the one or more processors, cause the one or more processors to adjust the image based on the sensor data indicative of the ambient lighting conditions to generate an adjusted image of the portion of the worksite ahead of the controllable valve and to control the controllable valve based on the adjusted image.

Abstract: An automated farming machine can operate in low light environments (e.g., at night) using one or more lights, attached to a boom, to illuminate the ground surface of a field for cameras of the machine. The cameras capture images of the field, which the machine processes by identifying a region of interest (ROI) within the image and a plant within the ROI. The boom moves as the machine goes over uneven ground, causing the brightness captured in the images to change. The machine may analyze a portion of the ground surface that is illuminated to identify an ROI within the image. The machine identifies the ROI by comparing the brightness of the image to a threshold brightness level. The machine takes actions based on the ROI, such as spraying herbicide on a weed identified in the ROI at a certain time after identifying the weed.

Abstract: An agricultural material application machine includes a material reservoir that holds a material to be applied to a field, an orifice defining an opening through which material is to pass to be applied to the field, an optical sensor that captures an image of a portion of the field, and a n image processor that processes the image and identifies a target in the image. The agricultural material application machine further includes a confidence level generator that generates a confidence level based on confidence data and a controller that generates a control signal to control the agricultural material application machine based on the confidence level.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied. A controller controls applicators, such as nozzles or other applicators, to apply the material to the sensed targets.

Abstract: The present disclosure relates to novel sialidase enzymes having a preference for cleaving N-glycolylneuraminic acid (Neu5Gc) from glycoconjugates over cleaving N-acetyineuraminic acid (Neu5Ac). The present disclosure further provides the polypeptide of such novel sialidase and its catalytic pockets; the recombinant host cells expressing the same, as well as the enzymatic, pharmaceutical, and consumable compositions comprising such novel sialidases. Probiotic compositions comprising a bacterium that expresses the sialidase having a preference for Neu5Gc over Neu5Ac, and methods for removing Neu5Gc from consumable products are also provided. Also disclosed are methods for treating or preventing various diseases, disorders, and inflammatory conditions in a subject using the novel sialidases.

Abstract: A camera is mounted on the front of a sprayer. Targets are identified from images generated by the camera. The location of the target is identified relative to a nozzle on the sprayer. A control signal is generated to control the timing and duration of actuation of the nozzle.

Abstract: In an agricultural machine vision system for the selective treatment of a field employing a boom deployed sensor and treatment device, the system utilizes at least one image sensor mounted on the boom to produce base image data. A boom angle detector detects boom height which is used to correct the base image data into remapped image data including a region of interest to produce corrected image data producing a regularized image of the region of interests for treatment, independent of boom tilt.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied. A controller controls applicators, such as nozzles or other applicators, to apply the material to the sensed targets.

Abstract: Systems and methods are provided for enhancing identification of crop conditions and execution of remedial actions in near real-time. An agricultural vehicle may include a crop analysis system having a computing device and an imaging array for capturing a set of images of a crop. The computing device analyzes images acquired by the imaging array to determine whether a crop experiences a particular condition. The computing device, when a condition is identified, can signal an agriculture machine to perform a remedial action.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied. A controller controls applicators, such as nozzles or other applicators, to apply the material to the sensed targets.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied.

Abstract: An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied.

Abstract: In an agricultural machine vision system for the selective treatment of a field employing a boom deployed sensor and treatment device, the system utilizes at least one image sensor mounted on the boom to produce base image data. A boom angle detector detects boom height which is used to correct the base image data into remapped image data including a region of interest to produce corrected image data producing a regularized image of the region of interests for treatment, independent of boom tilt.

Abstract: Systems and methods are provided for enhancing identification of crop conditions and execution of remedial actions in near real-time. An agricultural vehicle may include a crop analysis system having a computing device and an imaging array for capturing a set of images of a crop. The computing device analyzes images acquired by the imaging array to determine whether a crop experiences a particular condition. The computing device, when a condition is identified, can signal an agriculture machine to perform a remedial action.

Abstract: The present disclosure relates to novel sialidase enzymes having a preference for cleaving N-glycolylneuraminic acid (Neu5Gc) from glycoconjugates over cleaving N-acetylneuraminic acid (Neu5Ac). The present disclosure further provides the polypeptide of such novel sialidase and its catalytic pockets; the recombinant host cells expressing the same, as well as the enzymatic, pharmaceutical, and consumable compositions comprising such novel sialidases. Probiotic compositions comprising a bacterium that expresses the sialidase having a preference for Neu5Gc over Neu5Ac, and methods for removing Neu5Gc from consumable products are also provided. Also disclosed are methods for treating or preventing various diseases, disorders, and inflammatory conditions in a subject using the novel sialidases.

Abstract: The automated treatment of agricultural fields allows precise application of treatment to only areas of the field requiring treatment, allowing reduced use of agricultural products such as pesticides and the related reduction in environmental stress. Such automated treatment requires the monitoring of image information of the field to detect objects of interest such as weeds. Such monitoring of image information of the field suffers from the real world problem of shadows cast from the agricultural equipment and/or adjacent trees or structures that cast shadows across the field. Shadows are extracted from the image data by smoothing the data, and developing a shadow mask by employing an intensity histogram and identifying a local minima in the darker intensities of the histogram to identify a shadow threshold. The image areas within the shadow area detected by said mask are then corrected by multiplication with correction coefficients to extract the shadow.

Abstract: The automated treatment of agricultural fields allows precise application of treatment to only areas of the field requiring treatment, allowing reduced use of agricultural products such as pesticides and the related reduction in environmental stress. Such automated treatment requires the monitoring of image information of the field to detect objects of interest such as weeds. Such monitoring of image information of the field suffers from the real world problem of shadows cast from the agricultural equipment and/or adjacent trees or structures that cast shadows across the field. Shadows are extracted from the image data by smoothing the data, and developing a shadow mask by employing an intensity histogram and identifying a local minima in the darker intensities of the histogram to identify a shadow threshold. The image areas within the shadow area detected by said mask are then corrected by multiplication with correction coefficients to extract the shadow.