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: 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.