Abstract: A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Abstract: A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Abstract: A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Abstract: A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Abstract: A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Abstract: Cloud cover assessment system and method provides for automatically determining whether a target digital image acquired from remote sensing platforms is substantially cloud-free. The target image is acquired and compared to a corresponding known cloud-free image from a cloud-free database, using an optimized feature matching process. A feature matching statistic is computed between pixels in the target image and pixels in the cloud-free image and each value is converted to a feature matching probability. Features in the target image that match features in the cloud-free image exhibit a high value of feature matching probability, and are considered unlikely to be obscured by clouds, and may be designated for inclusion in the cloud-free database.

Abstract: Cloud cover assessment system and method provides for automatically determining whether a target digital image acquired from remote sensing platforms is substantially cloud-free. The target image is acquired and compared to a corresponding known cloud-free image from a cloud-free database, using an optimized feature matching process. A feature matching statistic is computed between pixels in the target image and pixels in the cloud-free image and each value is converted to a feature matching probability. Features in the target image that match features in the cloud-free image exhibit a high value of feature matching probability, and are considered unlikely to be obscured by clouds, and may be designated for inclusion in the cloud-free database.

Abstract: Cloud cover assessment system and method provides for automatically determining whether a target digital image acquired from remote sensing platforms is substantially cloud-free. The target image is acquired and compared to a corresponding known cloud-free image from a cloud-free database, using an optimized feature matching process. A feature matching statistic is computed between pixels in the target image and pixels in the cloud-free image and each value is converted to a feature matching probability. Features in the target image that match features in the cloud-free image exhibit a high value of feature matching probability, and are considered unlikely to be obscured by clouds, and may be designated for inclusion in the cloud-free database.

Abstract: A system and method processes original digital numbers (DNs) provided by a satellite imaging system to produce a set of spectral balanced and contrast enhanced multispectral images. Spectral balancing is achieved based on physical characteristics of sensors of the imaging system as well as compensation for atmospheric effects. The DNs in the multispectral bands may be processed using a relatively small amount of processing resources otherwise required to produce such images. Such images may be processed completely automatically and provide relatively easy visual interpretation. Each image pixel may be, for example, in an 8-bit or 16-bit format, and the image may be displayed and/or printed without applying any additional color correction and/or contrast stretches.

Abstract: A system and method processes original digital numbers (DNs) provided by a satellite imaging system to produce a set of spectral balanced and contrast enhanced multispectral images. Spectral balancing is achieved based on physical characteristics of sensors of the imaging system as well as compensation for atmospheric effects. The DNs in the multispectral bands may be processed using a relatively small amount of processing resources otherwise required to produce such images. Such images may be processed completely automatically and provide relatively easy visual interpretation. Each image pixel may be, for example, in an 8-bit or 16-bit format, and the image may be displayed and/or printed without applying any additional color correction and/or contrast stretches.

Abstract: A system and method processes original digital numbers (DNs) provided by a satellite imaging system to produce a set of spectral balanced and contrast enhanced multispectral images. Spectral balancing is achieved based on physical characteristics of sensors of the imaging system as well as compensation for atmospheric effects. The DNs in the multispectral bands may be processed using a relatively small amount of processing resources otherwise required to produce such images. Such images may be processed completely automatically and provide relatively easy visual interpretation. Each image pixel may be, for example, in an 8-bit or 16-bit format, and the image may be displayed and/or printed without applying any additional color correction and/or contrast stretches.