Methods and systems for selectively applying a treatment to a plant item is provided. The method may include conveying, in an industrial processing line, at least one plant item in the batch of plant items to a sensing region having one or more sensors, assessing, with the one or more sensors and a computing device, one or more properties of the at least one plant item associated with ripeness and/or another attribute, conveying the at least one plant item to a treatment region, determining a treatment to apply to the at least one plant item based on the assessed one or more properties; and applying, in the treatment region, the treatment to the at least one plant item.

A server apparatus 10 is communicably connected to a terminal apparatus 20 that collects sensor data from an odor sensor 40. The server apparatus 10 includes an analyzer holding unit 11 that holds a plurality of analyzers for analyzing specific odor analysis targets, based on sensor data, an analyzer management unit 12 that determines preprocessing to be performed on the sensor data, by selecting an analyzer according to the environment of the odor sensor 40, and causes the terminal apparatus 20 to execute the preprocessing, an analysis execution unit 13 that executes analysis processing of the designated odor analysis target, by applying the selected analyzer to the preprocessed sensor data, and an analysis result transmission unit 14 that transmits information indicating a result of the analysis processing to the terminal apparatus 20.

A measurement system is provided with an array of laser diodes with one or more Bragg reflectors. At least a portion of the light generated by the array is configured to penetrate tissue comprising skin. A detection system configured to: measure a phase shift, and a time-of-flight, of at least a portion of the light from the array of laser diodes reflected from the tissue relative to the portion of the light generated by the array; generate one or more images of the tissue; detect oxy- or deoxy-hemoglobin in the tissue; non-invasively measure blood in blood vessels within or below a dermis layer within the skin; measure one or more physiological parameters based at least in part on the non-invasively measured blood; and measure a variation in the blood or physiological parameter over a period of time.

This invention shows a system and method for phenotype characterization of agricultural crops, comprising at least one support device which can be reconfigured in an autonomous or controlled remote manner. It includes a central embedded microcontroller connected to atmospheric sensors located in the upper body, a microcontroller connected to a multi-spectral camera located at the distal end of the arm. The central microcontroller is connected to a base microcontroller that receives signals from soil sensors. A solar panel provides the energy source to a regulating unit that powers the microcontrollers. The system contains a communication unit that includes a router wirelessly connected to Internet.

The present disclosure relates to biosensors (10) having a receptor layer (5) and a mediator layer (6), the receptor layer including ethylene receptor molecules. The present disclosure also relates to sensor units (20) comprising one or more biosensors (10) and a controller (11). In some embodiments, one or more sensor units (20) may be in wireless communication with a receiver module or a network gateway.

The invention relates to a device for crumbling root crops into substantially equal sized pieces and homogenizing the flow of crumbled pieces for analysis. The device can have a crumbling device with a main frame and at least one crumbling shaft rotatably supported in the main frame. The crumbling shaft can have a plurality of curved hooks for interlaced movement with recesses in at least one non-rotating cutting rake and at least one cleaning rake. A transport device can move the stream of crumbled root crops from the crumbling device where the stream can be leveled with a leveling rake and compressed into a substantially flat and uniform flow with a roller. The roller can have one or more scrapers positioned to clean the roller surface of residual root crop product and distribute the cleaned material in locations away from the stream to be analyzed to avoid contamination.

The invention relates to an inspection system for quality analysis of a food product, comprising conveyance means for moving it to an inspection area, a lighting device consisting of LEDs that emit a light sequence directed towards the inspection area to light up the product by transmission, a linear camera with at least one line of pixels for collecting a plurality of images in each light sequence of the lighting device, and focusing means for focusing the beam emitted by the lighting device. Advantageously, the lighting device is activated in a pulsed manner, generating a light sequence with at least two different illuminations, at least one of which is a transmission-pulsed illumination. The lighting device is aligned on the axis formed by the product to be inspected and the linear camera.

The present invention relates to a method for identifying fruit shelf life automatically based on thermal imaging Identifying every day the pattern of change in temperature of fruit's thermal image, we can predict the shelf life of fruit which is how many days the fruit will remain edible in an accurate manner without destructing the fruit. In this system, thermal dataset is created comprising of samples of thermal images (206) of fruit taken on every day after harvesting where fruit may be from cold storage or room temperature using thermal imaging device (204). Transfer learning a deep learning technique is applied on this thermal dataset to compute the threshold weights (210) which are then used to classify or predict the fruit shelf life by comparing these pre-trained weights with the features of fruit's thermal image extracted from convolution and poling layer (212) of deep learning model

The present application is related to the process of automation of separation by identification, classification and quantification of grains and their possible pertinent materials through equipment that performs such events, aiming at the automation of the whole chain of separation, identification and classification. grain, thus eliminating the human action of the process and thus avoiding errors related to human interaction in the process. This process has 4 steps, as follows: grain and impurities entering the equipment; separation of impurities and grains: grain processing and qualitative and quantitative identification of grains and impurities. The process and equipment can be applied to the separation by identification, classification and quantification of grains such as soybeans, corn, among others, and their possible pertinent materials.

Provided is a modeling method and apparatus for a model of tracing the origin of durians, and a method for tracing the origin of durians. The modeling method includes obtaining isotope analysis data of pulp and seeds of durians of a target production area; generating a model of tracing the origin for a corresponding target production area through a preset analysis modeling algorithm using the isotope analysis data and information of the corresponding target production area; and validating an accuracy of the model of tracing the origin using the isotope analysis data of the target production area and other production areas, to obtain the accuracy of the model. The modeling method for a model of tracing the origin of durians of the present application performs isotope analysis on durians of the target production area, and generates a corresponding model of tracing the origin after obtaining isotope analysis data.