Systems and methods for generating and using statistical models to mitigate atmospheric effects in images are described. In some embodiments, a statistical model may be generated by selecting a vegetation type that grows in continuous healthy canopies; identifying a vegetation reference value that is a stable reflectance property of the vegetation type; in a plurality of images, selecting one or more plots of the vegetation type and obtaining top-of-atmosphere reflectance for the plots; selecting discrete areas near the plots and obtaining top-of-atmosphere reflectance for the discrete areas; obtaining image statistics for the discrete areas; and generating a statistical model based on the acquired data.

Described herein are mobile sensing units for capturing raw data corresponding to certain characteristics of plants and their growing environment. Also described are computer devices and related methods for collecting user inputs, generating information relating to the plants and/or growing environment based on the raw data and user inputs, and displaying same.

The present disclosure relates to the field of image processing and discloses an agricultural advisory system (100) comprising a user device (102) and a cloud server (104). The user device (102) captures a digital image of a scene, receives a sensed data corresponding to scene-related and environmental parameters, and transmits the image and the sensed data to the cloud server. The server (104) stores one or more pre-trained prediction models and a three-dimensional HyperIntelliStack which maps red green blue (RGB) pixel values with hyperspectral reflectance values. The server (104) receives the digital images and the sensed data, transforms the received image made of RGB pixel values into a hyperspectral image using the HyperIntelliStack data structure, computes vegetation indices for each pixel of the hyperspectral image to generate a segmented image, and generates at least one advisory for agriculture and allied areas using the segmented image and one or more prediction models.

A system and a method for removing haze from remote sensing images are disclosed. One or more hazy input images with at least four spectral channels and one or more target images with the at least four spectral channels are generated. The one or more hazy input images correspond to the one or more target images, respectively. A dehazing deep learning model is trained using the one or more hazy input images and the one or more target images. The dehazing deep learning model is provided for haze removal processing.

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.

An agricultural harvesting machine includes crop processing functionality configured to engage crop in a field, perform a crop processing operation on the crop, and move the processed crop to a harvested crop repository, and a control system configured to identify a weed seed area indicating presence of weed seeds, and generate a control signal associated with a pre-emergence weed seed treatment operation based on the identified weed seed area.

A computer system is provided comprising a classification model management server computer configured, by instructions, to: receive a new image from a user device; apply a first digital model to first regions within the new image for classifying each of the first regions into a particular class; apply a second digital model to second regions within the new image for classifying each of the second regions into a particular class; and transmit classification data related to the class of the first regions and the class of the second regions to the user device. In connection therewith, the second regions each generally correspond to a combination of multiple first regions.

System and methods to control resource distribution to fields are provided. A data processing system can identify a plurality of zones of the field, and can receive, from a client computing device, registration information of a node. The registration information can correlate the node with a zone of the field. The data processing system can obtain, via a gateway device and from the node, a first data file including field metric data and a second data file including third party data that pertains to the field. The data processing system can determine a resource distribution schedule based at least in part on data of the first data file and data of the second data file. The data processing system can receive, via the gateway device, a prompt from the node, and can provide an instruction to control a valve to regulate resource distribution to a portion of the field.

A method for regulating illumination of plants, an electronic device, and a storage medium are disclosed. In the method, plant images are inputted into a growth calculation model to obtain a target growth parameter, the target growth parameter is analyzed according to a state shunt model to obtain a first physiological stage, a growth change value of the target plant is calculated according to target growth parameters, the first physiological stage is adjusted according to the growth change value to obtain a second physiological stage, a target illumination time corresponding to the second physiological stage is generated based on the target growth model, a target time period is generated according to the light regulation model and the target illumination time, the target time period is send to an illumination device to regulate illumination of the target plant.

Methods and systems used for the classification of a crop grown within an agricultural field using remotely-sensed image data. In one example, the method involves unsupervised pixel clustering, which includes gathering pixel values and assigning them to clusters to produce a pixel distribution signal. The pixel distribution signals of the remotely-sensed image data over the growing season are summed up to generate a temporal representation of a management zone. Location information of the management zone is added to the temporal data and ingested into a Recurrent Neural Network (RNN). The output of the model is a prediction of the crop type grown in the management zone over the growing season. Furthermore, a notification can be sent to an agricultural grower or to third parties/stakeholders associated with the grower and/or the field, informing them of the crop classification prediction.