The present invention provides a screening method for selecting plant symbiotic microorganisms enabling plant growth under abiotic stress and/or biotic stress, a microbial mixture of microorganisms which are selected from nature by the method and enables a plant to grow under salinity stress and/or hyperosmotic stress in symbiosis with the plant. Specifically, the present invention provides a screening method for selecting plant symbiotic microorganisms, comprising a first screening step of allowing at least one plant to grow for a predetermined period of time while being partially soaked in a solution containing a microbial mixture under abiotic stress and/or biotic stress, and collecting microorganisms adhering to grown plant to thereby select out the microorganisms as plant symbiotic microorganisms enabling plant growth under the stress.

The present invention provides a screening method for selecting plant symbiotic microorganisms enabling plant growth under abiotic stress and/or biotic stress, a microbial mixture of microorganisms which are selected from nature by the method and enables a plant to grow under salinity stress and/or hyperosmotic stress in symbiosis with the plant. Specifically, the present invention provides a screening method for selecting plant symbiotic microorganisms, comprising a first screening step of allowing at least one plant to grow for a predetermined period of time while being partially soaked in a solution containing a microbial mixture under abiotic stress and/or biotic stress, and collecting microorganisms adhering to grown plant to thereby select out the microorganisms as plant symbiotic microorganisms enabling plant growth under the stress.

A microneedle probe for measuring a sap flow in a plant is disclosed, the microneedle probe including: a substrate; and a sensor unit which is installed on the substrate, generates heat, and measures a temperature that changes in accordance with a sap flow.

A microneedle probe for measuring a sap flow in a plant is disclosed, the microneedle probe including: a substrate; and a sensor unit which is installed on the substrate, generates heat, and measures a temperature that changes in accordance with a sap flow.

A crop yield prediction program includes: a degree-of-association acquisition step of acquiring in advance a degree of association between a combination of reference image information which is a captured image of a growing crop and reference soil information about a soil in which the crop is planted and a yield of the growing crop as harvested for the combination, the degree of association being represented in three or more levels; an information acquisition step of, when making a new prediction of the yield of the crop, capturing an image of a new growing crop to acquire image information and to acquire soil information about a soil in which the crop is planted; and a prediction step of predicting a yield of the new growing crop with reference to the degree of association acquired at the degree-of-association acquisition step and based on the image information and the soil information.

A crop yield prediction program includes: a degree-of-association acquisition step of acquiring in advance a degree of association between a combination of reference image information which is a captured image of a growing crop and reference soil information about a soil in which the crop is planted and a yield of the growing crop as harvested for the combination, the degree of association being represented in three or more levels; an information acquisition step of, when making a new prediction of the yield of the crop, capturing an image of a new growing crop to acquire image information and to acquire soil information about a soil in which the crop is planted; and a prediction step of predicting a yield of the new growing crop with reference to the degree of association acquired at the degree-of-association acquisition step and based on the image information and the soil information.

The disclosure provides a method of screening tobacco germplasm for resistance to Alternaria alternata by ripening seedling leaves. The method includes ripening seedling leaves, spray inoculation, disease induction, and evaluation of disease resistance. Dense planting, fertilizer control, and potassium increment were used to forcibly ripening seedling leaves. A hospitable environment was simulated to induce disease in the ripened leaves. These treatments reduce differences in leaf maturity and avoid environmental changes. The technique of the disclosure provides greater accuracy and repeatability than the current technique of screening brown spot resistance, and offers the advantages of simple operation, reduced cost, space requirement, and labor intensity, high selection efficiency, and an accurate screening of tobacco phenotypes with resistance to brown spot, etc., used for large-scale screening of tobacco varieties with resistance to brown spot.

One variation of a method for identifying stressors in crops based on fluorescence of sensor plants includes: accessing a set of spectral images of a sensor plant sown in a crop, the sensor plant of a sensor plant type including a set of promoters and a set of reporters configured to signal a set of stressors present at the sensor plant, the set of promoters and set of reporters forming a set of promoter-reporter pairs; accessing a reporter model linking characteristics extracted from the set of spectral images of the sensor plant to the set of stressors based on signals generated by the set of promoter-reporter pairs in the sensor plant type; and identifying a first stressor, in the set of stressors, present at the sensor plant based on the reporter model and characteristics extracted from the set of spectral images.

There is provided with an information processing system including a server apparatus, and a first apparatus and a second apparatus that are able to communicate with the server apparatus. A management unit collects, based on a predetermined reference, each piece of data that is acquired from the first apparatus and is associated with each of a plurality of images and manage the collected data. An image identification unit identifies at least a portion of the plurality of images based on the collected data. A sending unit sends the at least a portion of the plurality of images that is identified by the image identification unit, to the second apparatus.

A prediction system according to an embodiment is configured to: acquire a plurality of input vectors indicating a combination of an object feature represented by one or more feature quantities related to a state of an object calculated based on an observation and an environmental feature represented by one or more feature quantities related to a surrounding environment of the object; divide a set of the environmental features into a plurality of clusters by clustering; and executing machine learning for each of the plurality of input vectors to generate a machine learning model for predicting state of object. The machine learning includes: executing processing based on the cluster to which the environmental feature of the input vector belongs; and outputting a predictive value of the state of the object by inputting the input vector into the machine learning model on which the processing is executed.