The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.

In some embodiments, a system for remotely purchasing perishable products includes a plurality of image capture devices configured to capture images of perishable products, a product image database, a product attribute database, and a control circuit configured to execute computer program modules. An image processing module receives and processes images from the image capture devices to form a composite image. A product identification module identifies and segments the products in the composite image. A product selection module causes an application executed on a remote electronic device operated by a customer to display the composite image and receives from the application a selection of a product to purchase, at least one product attribute, and a quantity of individual product units to purchase. The product selection module identifies the individual product units having the selected product attribute(s) and outputs signaling to cause retrieval of the individual product units.

A method of reality augmentation, including: (a) determining the identity of a container, for example chocolate spread, with a top opening; (b) acquiring an image of the container from a top thereof; (c) estimating a geometry of the filling of said container based on said identity and said image; and (d) overlaying an augmentation, for example, a coupon or a toy, on an image, based on said estimation.

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.

A method for generating a quality inspection data block for a distributed ledger includes: determining an identification code associated with a sample to be inspected, inspecting the sample and thereby generating quality inspection data associated with the sample, and after completion of the inspecting of the sample combining the identification code and the quality inspection data into the quality inspection data block. The method also includes adding the quality inspection data block to the distributed ledger. An inspector including a sensor that senses a characteristic of a sample, a memory that stores sensor output data, and a processor configured to: determine an identification code associated with a sample to be inspected, generate quality inspection data based on the sensor output data, and combine the identification code and the quality inspection data into a quality inspection data block. In one example, the inspector is an in-flight 3D inspector.

Devices, systems, and methods for real-time food production are disclosed. Extrusion can include including evaluating and controlling one or more production devices to produce desirable food products. Evaluation can be performed by an evaluation system including a convolutional neural network to determine a bulk density value. Control can be performed by a machine learning model on the basis of the bulk density value. Control can include determination of real-time settings for production device parameters.

A system for monitoring gluten consumption, especially in celiac people, which allows the feeding of food consumption data and updating, in real time, of the estimated amount of gluten consumed daily. Still, the present invention refers to a system for prediction that associates the possibility of an indisposition being associated or not with an undue consumption of gluten.

A method and a system for determining an indicator of processing quality of an agricultural harvested material using a mobile device is disclosed. A computing unit analyzes image data of a prepared sample of harvested material containing grain components and non-grain components in an analytical routine to determine the indicator of the processing quality of the agricultural harvested material. Further, the computing unit uses a trained machine learning model in the analytical routine to perform at least one step of determining the indicator of the processing quality of the agricultural harvested material.

A method in a computing device includes: in a facility containing a plurality of support structures, capturing an image of a first support structure; detecting, in the image, a first feature set of the first support structure; selecting obtaining at least one reference feature set by proximity to an estimated location of the mobile computing device in the facility coordinate system, the at least one reference feature set selected from a repository defining feature locations for each of the support structures in a facility coordinate system; comparing the first feature set with the at least one reference feature set; and in response to determining that the first feature set matches the at least one reference feature set, determining a location of the mobile computing device in the facility coordinate system based on the image and the feature locations from the repository.

Intelligence guided system and method for fruits and vegetables processing includes a conveyor for carrying produces, various image acquiring and processing hardware and software, water and air jets for cutting and controlling the position and orientation of the produces, and a networking hardware and software, operating in synchronism in an efficient manner to attain speed and accuracy of the produce cutting and high yield and low waste produces processing. The 2nd generation strawberry decalyxing system (AVID2) uniquely utilizes a convolutional neural network (AVIDnet) supporting a discrimination network decision, specifically, on whether a strawberry is to be cut or rejected, and computing a multi-point cutline curvature to be cut along by rapid robotic cutting tool.