The disclosure relates to a method for analyzing a plant, in particular for analyzing cannabis, using an illumination unit, a sensor unit, and an analysis unit, said analysis unit having a data-based classifier. The disclosure additionally relates to a device for analyzing a plant, said device comprising an illumination unit for lighting the plant to be analyzed and a sensor unit for receiving analysis input data, wherein the analysis input data contains at least spectral information, in particular an absorption spectrum or a reflection spectrum of the training plant. The device additionally comprises an analysis unit for analyzing the received analysis input data and for determining at least one property of the plant to be analyzed. The analysis unit is also designed to determine at least one property of the plant using a data-based classifier and the previously received analysis input data.

A chlorophyll fluorescence measuring system having at least one spectrometer coupled to a data logger. The data logger provides direct control of the spectrometer and includes on-board memory for storage of target and reference spectrum data obtained by the spectrometer. The data logger may be coupled to an external computer that receives and analyzes target and reference spectrum data to determine SIF using a spectral fitting algorithm. The system may include a spectrometer aiming system coupled to and controlled by the data logger. The system may also include one or more environmental sensors configured to measure environment variables. The environmental sensors may be coupled to the data logger for control and data storage. The environmental data may be communicated to the external computer for use in the spectral fitting algorithm. The data logger may be connected to a network for remote monitoring and control.

The disclosure discloses a method for near-infrared spectral wavelength selection based on an improved team progress algorithm (iTPA), belonging to the field of near-infrared spectral detection. The method includes: equally dividing near-infrared spectral wavebands to be selected into elite groups, ordinary groups and garbage collection groups according to evaluation values from high to low; generating a new waveband in the elite group or the ordinary group, where a wavelength point of the new waveband is selected from a random waveband in the selected group, and the wavelength point of the new waveband inherits the selected wavelength point; and enabling the inherited new waveband to select a learning behavior or an exploration behavior according to a set probability to update the wavelength point of the new waveband to generate a candidate waveband; and selecting a waveband with a highest evaluation value in the elite group as the waveband to be selected. Under the condition of ensuring the model prediction accuracy, the disclosure greatly reduces the number of wavelength variables, reduces the complexity of the algorithm at the same time, and improves the non-destructive detection accuracy in crops.

An apparatus for detecting a plant health status is described. The apparatus includes a light source positioned relative to the plant and a detector positioned relative to the plant. The light source is configured to emit a white light or an ultraviolet light. The ultraviolet (UV) light corresponds to UV-A and has a wavelength in the range of 340 nanometers (run) to 400 nm. The detector is configured to receive evaluation light. The evaluation light is related to the emitted light and corresponds to a health status of the plant.

The present invention generally relates to detecting a root of a plant in soil. An exemplary system comprises a support structure configured to be at least partially disposed in the soil; an LED unit affixed to the support structure, wherein the LED unit comprises an emitter and a detector, wherein the emitter is configured to produce a plurality of outgoing light signals, wherein the detector is configured to receive a plurality of returned light signals corresponding to the plurality of outgoing light signals, and wherein each of the plurality of returned light signals comprises at least a portion of the corresponding outgoing light signal reflected from at least one of the soil and the root; and a microprocessor configured to detect a presence of the root based on the plurality of returned light signals.

An IoT based apparatus to assess the quality of food or agricultural produce is disclosed. The apparatus comprises a frame having an enclosure, a movable tray disposed at a middle portion of the enclosure, one or more camera installed within the enclosure, one or more light source strategically mounted within the enclosure to avoid reflection and hot spots, at least one storage unit, a touch screen display and a single board computer coupled to the camera, light source, storage unit and touch screen display. The enclosure is illuminated by the light source and the camera is configured to capture the image of the produce from both the top and bottom side of the produce. A pre-trained deep learning model is used on both the top and bottom view images to identify defects in the agricultural produce. The identified defects are displayed to the user via the touch screen display.

An image processing method includes acquiring an image obtained by imaging of an object, ambient light data during the imaging, and reflection characteristic data and transmission characteristic data which depend on a concentration of a material contained in the object, and separating a reflected light component and a transmitting light component in the image using the ambient light data, the reflection characteristic data, and the transmission characteristic data.

An agricultural machine is disclosed that includes an NIR sensor configured to detect NIR spectra of plant material and output them as raw data, an evaluation unit configured to derive at least one parameter of the plant material in real time from the raw data, and an interface for the data traffic with at least one data processing unit outside of or external to the agricultural machine that is configured to transmit the raw data to the data processing unit.

The present disclosure relates to a method and system for the in-situ detection of cadmium chelates in a plant. The method includes: preparing cadmium chloride and phytochelatin (PC) 2 samples with different concentrations; using terahertz time-domain spectroscopy (THz-TDS) to determine a terahertz (THz) reflection image for the cadmium chloride and PC 2 samples; building a cadmium chloride content detection model according to first spectral data; building a PC 2 content detection model according to second spectral data; determining an optimal mixed sample according to the cadmium chloride content detection model and the PC 2 content detection model; building a Cd—PC.sub.2 content detection model according to the optimal mixed sample; and using the Cd—PC.sub.2 content detection model to determine a cadmium chelate content in a to-be-tested plant. The present disclosure can quickly and in-situ detect the heavy metal accumulation capacity of a plant.

A scan using a pulsed distance measuring light with two wavelengths of which reflectances are different with respect to a content of a nitrogen by a laser scanner, the two wavelengths are separated, lights are received, a distance measurement value and a light amount are detected for each pulsed distance measuring light and for each of the two wavelengths, a height of a crop is detected based on the distance measurement value, a received light amount ratio of the two wavelengths is detected, and a growth condition of the crop is detected based on the detected height and the received light amount ratio.