Abstract: Disclosed herein are methods and systems for anomaly detection within a field using images or sensor data. An anomaly detection process determines whether a pixel or a cluster of pixels fit within calculated or modeled expectations to identify an anomaly. The process can further analyze the anomaly to determine a likely cause. Example anomaly detection techniques account for intra-field inhomogeneity and for non-crop regions. Example anomaly detection techniques also can identify anomalous regions.

Abstract: A method for assessing at least one condition of a plant material is provided. The method may include providing a spectral image, normalizing the spectral image to generate a normalized spectral image, processing the normalized spectral image to generate at least one processed spectral image, providing a palette file, and generating a displayable false color image from the at least one processed spectral image and the palette file. The method may include capturing a multi-spectral image of the plant material with a multi-spectral imager. The plant material may be a portion of a cannabis plant and the condition may be one of, or relating to, health. A non-transitory computer readable storage medium storing a computer program for performing the method is also provided.

Abstract: Disclosed herein are methods and systems for anomaly detection within a field using images or sensor data. An anomaly detection process determines whether a pixel or a cluster of pixels fit within calculated or modeled expectations to identify an anomaly. The process can further analyze the anomaly to determine a likely cause. Example anomaly detection techniques account for intra-field inhomogeneity and for non-crop regions. Example anomaly detection techniques also can identify anomalous regions.

Abstract: Disclosed herein are methods and systems for anomaly detection within a field using images or sensor data. An anomaly detection process determines whether a pixel or a cluster of pixels fit within calculated or modeled expectations to identify an anomaly. The process can further analyze the anomaly to determine a likely cause. Example anomaly detection techniques account for intra-field inhomogeneity and for non-crop regions. Example anomaly detection techniques also can identify anomalous regions.

Abstract: Disclosed herein are methods and systems for anomaly detection within a field using images or sensor data. An anomaly detection process determines whether a pixel or a cluster of pixels fit within calculated or modeled expectations to identify an anomaly. The process can further analyze the anomaly to determine a likely cause. Example anomaly detection techniques account for intra-field inhomogeneity and for non-crop regions. Example anomaly detection techniques also can identify anomalous regions.

Abstract: Disclosed herein are methods and systems for anomaly detection within a field using images or sensor data. An anomaly detection process determines whether a pixel or a cluster of pixels fit within calculated or modeled expectations to identify an anomaly. The process can further analyze the anomaly to determine a likely cause. Example anomaly detection techniques account for intra-field inhomogeneity and for non-crop regions. Example anomaly detection techniques also can identify anomalous regions.