Abstract: Described herein are systems and methods for determining dryness of produce using image data. A method can include receiving, by a computing system and from an imaging device, image data of a batch of produce, performing, by the computing system, object detection to identify each produce in a frame of the image data, extracting, by the computing system, temperature values in pixels of the identified produce in the frame, and determining, by the computing system, distribution characteristics of the extracted temperature values. The method can also include predicting, by the computing system, a dryness metric for the batch of produce based on applying a trained model to the determined distribution characteristics. The model can be trained using temperature distributions of other produce, the temperature distributions being annotated based on previous mappings of skewness of the temperature distributions to dryness.

Abstract: Described herein are systems and methods for determining quality levels for food items using image data, such as time lapse RGB, hyperspectral, thermal, and/or multispectral images. The method can include receiving, from imaging devices, image data of food items, performing object detection on the image data to identify a bounding box around each food item, and identifying a quality level of each food item by applying trained models to the bounding boxes. The models were trained using image training data of other food items that was annotated based on previous identifications of a first portion of the other food items as having poor quality features and a second portion as having good quality features. The other food items and the food items are a same type. The method also includes determining, for each food item, a quality level score based on the identified quality level of the food item.

Abstract: Disclosed are techniques for determining object throughput. A method may include obtaining first data representing a first image corresponding to a first time, identifying a first portion of the first data that depicts a first object at a first location, obtaining second data representing a second image corresponding to a second time, identifying a second portion of the second data that depicts the first object at a second location, obtaining third data indicating a counting threshold, determining based at least on the third data and the second location, that the first object satisfies the counting threshold, generating a value indicating a number of objects satisfying the counting threshold, the number of objects including the first object, generating a data value indicating a throughput of the number of objects based on the value indicating the number of objects satisfying the counting threshold and elapsed time between the first and second times.