A bale monitoring system includes an imaging device that is operable to capture a stereoscopic image of a bale. A computing compares the stereoscopic image of the bale to a three-dimensional standard to identify a deviation of the bale from the three-dimensional standard. The computing device may then assign the bale a shape quality score based on the deviation of the bale from the three-dimensional standard. The shape quality score may indicate a magnitude of the deviation of the bale from the three-dimensional standard. Additionally, the stereoscopic image may be analyzed to identify characteristics of the bale, such as a broken wrap material or an improperly formed bale.

A method for encoding dot-matrix product information method includes identifying, via a processor, a dot-matrix grid size. The method further includes evaluating, via the processor, one or more dot pattern variation levels. In some aspects, the method includes retrieving, via the processor, an encoding structure indicative of a plurality of product information attributes. The method also includes determining, via the processor, whether an alpha-numeric digit at a dot pattern variation level can include a plurality of product information. The method further includes outputting, via the processor, a dot pattern code map. In some aspects, the dot pattern code map is indicative of a relationship between each of the product information attributes and the plurality of values for each of the product information attributes.

In some embodiments, apparatuses and methods are provided herein useful to ensuring quality of meat cuts. In some embodiments, a system for ensuring quality of meat cuts comprises a capture device comprising an image capture device configured to capture an image of a cut of meat, a depth sensor configured to capture depth data, a transceiver configured to transmit the image and the depth data, a microcontroller configured to control the image capture device, the depth sensor, and the transceiver, a database configured to store meat cut specifications, and the control circuit configured to receive, from the capture device, the image and the depth data, retrieve, from the database, a meat cut specification, evaluate the image of the cut of meat and the depth data associated with the cut of meat, and classify the cut of meat.

In some embodiments, apparatuses and methods are provided herein useful to ensuring quality of meat cuts. In some embodiments, a system for ensuring quality of meat cuts comprises a capture device comprising an image capture device configured to capture an image of a cut of meat, a depth sensor configured to capture depth data, a transceiver configured to transmit the image and the depth data, a microcontroller configured to control the image capture device, the depth sensor, and the transceiver, a database configured to store meat cut specifications, and the control circuit configured to receive, from the capture device, the image and the depth data, retrieve, from the database, a meat cut specification, evaluate the image of the cut of meat and the depth data associated with the cut of meat, and classify the cut of meat.

A device and a method for detecting cigarette fly ash by a gray-scale difference based on machine vision (MV) are provided. A manipulator holds a cigarette as a detection sample to simulate a human smoking action, and multiple groups of cameras track a simulated smoking process of the detection sample synchronously in real time. It is determined whether fly ash appears based on a gray-scale difference of burning ash columns, produced without being subjected to flicking, in acquired images. A fly ash area of the burning ash columns of the cigarette is calculated by an area with the gray-scale difference of the burning ash columns of the cigarette in two sequential images of the burning ash columns of the cigarette, and a fly ash amount is further determined. The detection of the amount of fine fly ash is converted into the detection of the gray-scale difference.

Vacuum seal packages can be classified based on image data. Training image data is received that includes image data about first vacuum seal packages. Labels associated with the first vacuum seal packages are received, where each of the labels includes a state of one of the first vacuum seal packages. A trained classification model is developed based on the training image data and the received labels. Image data representative of a second vacuum seal package is received. The image data is inputted into the trained classification model, where the trained classification model is configured to classify a state of the second vacuum seal package based on the image data. The state of the second vacuum seal package is received from the trained classification model.

A food inventory device having a bar code scanner and camera to add and remove inventory items to and from the inventory. The scanner may be mounted on an inventory storage container to facilitate inventory control from one or more storage devices, including refrigerators, freezers, and pantries. The inventory system may display a table listing the inventory items along with variable details. Based on the food inventory list, the inventory system may suggest recipes, display the ingredients in stock for suggested recipes, and create a shopping list of the ingredients not in stock for a desired recipe. The device may have an ordering system allowing users to order items in the storage device that have been exhausted or otherwise need to be replenished through audio or touch input. The ordering system may be connected to third-party external sites and may be based on one or more user profile settings.

System and methods for monitoring the growth of an aquatic plant culture and detecting real-time characteristics associated with the aquatic plant culture aquatic plants. The systems and methods may include a control unit configured to perform an analysis of at least one image of an aquatic plant culture. The analysis may include processing at least one collected image to determine at least one physical characteristic or state of an aquatic plant culture. Systems and methods for distributing aquatic plant cultures are also provided. The distribution systems and methods may track and control the distribution of an aquatic plant culture based on information received from various sources. Systems and methods for growing and harvesting aquatic plants in a controlled and compact environment are also provided. The systems may include a bioreactor having a plurality of vertically stacked modules designed to contain the aquatic plants and a liquid growth medium.

A method for analysis of algae, comprising: receiving a microscopic image of algae by a cloud server (2501), the microscopic image including a scaling pattern for determining a magnification; determining the magnification by the cloud server based on the scaling pattern (2502); and analyzing the microscopic image by the cloud server based on the magnification to obtain an analysis result (2503).

A management system (1) manages a production line (2) including a processing apparatus (20) and an inspection apparatus (40). The management system (1) includes: a control unit (61) that estimates a processing time at which a defective food is processed by the processing apparatus (20) based on a time at which the defective food determined as a defective product by inspection by the inspection apparatus (40) is inspected; and a storage unit (62) that stores processing information of the processing apparatus (20) related to the processing time estimated by the control unit (61) such that the processing information can be output.