A portable apparatus for analyzing a plant specimen. A housing assembly defines a sensing volume and controls entry of ambient light into the sensing volume when the housing is closed. A specimen support positions a plant specimen within the sensing volume whereby light emitted from at least one light emitter is incident upon the plant specimen. An image sensor senses light from the at least one light emitter that has been incident on the plant specimen. A processor analyzes data obtained from the light sensor to assess one or more properties of the plant specimen. There may be more than one light emitter, e.g., a halogen lamp and LED array, and the apparatus may acquire images under more than one lighting condition. The apparatus may include a mechanism for moving the plant specimen relative to the optical path to take images at multiple regions of interest on the specimen.

One variation of a method for interpreting pressures in plants includes: accessing a first image of a first set of sentinel plants in a field; accessing a second image of a second set of sentinel plants in the field, recorded during a first period; interpreting a first pressure of a stressor in the first set based on features extracted from the first image, captured during the first period; interpreting a second pressure in the second set based on features extracted from the second image; deriving a model associating pressure at the first set and pressure at the second set based on the first pressure and the second pressure; interpreting a third pressure in the first set based on features extracted from a third image captured during a second period; and predicting a fourth pressure in the second set during the second period based on the third pressure and the model.

A method includes receiving first sensor data pertaining to plant-related parameters of each of one or more first plants that performed well over time. The method also includes analyzing at least some of the first sensor data to generate a predictive model associated with the one or more first plants. The method further includes receiving second sensor data pertaining to plant-related parameters of each of multiple second plants. In addition, the method includes identifying at least one of the second plants to receive one or more interventions by applying the predictive model to the second sensor data. Identifying the at least one of the second plants includes identifying the at least one of the second plants as having at least one of the plant-related parameters that deviates from at least one of the plant-related parameters of the one or more first plants.

A method includes receiving first sensor data pertaining to plant-related parameters of each of one or more first plants that performed well over time. The method also includes analyzing at least some of the first sensor data to generate a predictive model associated with the one or more first plants. The method further includes receiving second sensor data pertaining to plant-related parameters of each of multiple second plants. In addition, the method includes identifying at least one of the second plants to receive one or more interventions by applying the predictive model to the second sensor data. Identifying the at least one of the second plants includes identifying the at least one of the second plants as having at least one of the plant-related parameters that deviates from at least one of the plant-related parameters of the one or more first plants.

A method for performing machine vision recognition activities for a dynamic object is disclosed, which includes the steps of (i) creating a digital image series that includes a seed digital image set including digital images and at least one subsequent digital image set including digital images; (ii) creating an object detector series, said creating step (ii) comprising a) creating a seed object detector from the seed digital image set, the seed object detector comprising an architecture and a set of weights directed to recognition of target objects; and b) creating at least one deep learning object detector from the at least one subsequent digital image set and derived from said seed object detector, the deep learning object detector including a deep learning architecture and a set of weights trained for recognition of the evolution of the target objects over time.

A prediction apparatus (10) of the present disclosure includes a memory (12) storing a prediction model for calculating a predicted value of a harvest result including harvest weight and information on physiological disorders of plants in a plant factory (80), and a value of an environmental parameter indicating an environment of the plant factory (80), a controller (11) configured to input the value of the environmental parameter retrieved from the memory (12) into the prediction model to calculate the predicted value of the harvest result, and a communication interface (13) configured to output the predicted value of the harvest result calculated by the controller (11).

A working electrode is inserted into the plant to monitor the health of the plant components or to detect physical, mechanical damage or environmental change in the soil or atmosphere. A standard electrode is inserted into soil surrounding the plant or in the plant itself. A data logger connects the working electrode and the standard electrode. The data logger measures the potential difference between the working electrode and the electrolyte to provide the ability to compare a measured potential difference with a predetermined critical potential difference for the plant. A second electrochemical cell can inject electrons and ions into the plant. The plant can be used as a sensor to monitor the environmental change in the soil or in the atmosphere.

A method for increasing yield or vigor of a plant is provided, which includes the selective removal of the plant's shoot apical meristem at a period between vegetative growth stage 1 and vegetative growth stage 2, or between vegetative growth stage 2 and vegetative growth stage 3.

A method for increasing yield or vigor of a plant is provided, which includes the selective removal of the plant's shoot apical meristem at a period between vegetative growth stage 1 and vegetative growth stage 2, or between vegetative growth stage 2 and vegetative growth stage 3.

An imaging diagnosis apparatus includes a first acquiring unit configured to acquire information on the number of leaves of a plant using an image of the plant, and a second acquiring unit configured to acquire the information on the number of stems of the plant using the information on the number of leaves.