There is provided a method of assessing a characteristic of a plant (2), comprising: obtaining a training dataset, wherein the training dataset comprises first data characterising a first electrical signal obtained from a first plant during a first time period when a stressor is present in the first plant or a growth environment of the first plant, second data characterising a second electrical signal obtained from the first plant during a second time period when a stressor is not present in the first plant or the growth environment of the first plant, and third data indicative of a characteristic of the first plant during the first time period and a characteristic of the first plant during the second time period; training a machine learning model based upon the training dataset; obtaining a third electrical signal from a second plant; and assessing, using the trained machine learning model, a characteristic of the second plant based upon the third electrical signal.

There is provided a method of assessing a characteristic of a plant (2), comprising: obtaining a training dataset, wherein the training dataset comprises first data characterising a first electrical signal obtained from a first plant during a first time period when a stressor is present in the first plant or a growth environment of the first plant, second data characterising a second electrical signal obtained from the first plant during a second time period when a stressor is not present in the first plant or the growth environment of the first plant, and third data indicative of a characteristic of the first plant during the first time period and a characteristic of the first plant during the second time period; training a machine learning model based upon the training dataset; obtaining a third electrical signal from a second plant; and assessing, using the trained machine learning model, a characteristic of the second plant based upon the third electrical signal.

A dormancy control system for use with plants such as fruit trees, nut trees, and perennial berry bushes planted in commercial settings. The dormancy control system employs vertical shade structures to control the dormancy of plants such as fruit trees, nut trees, and perennial berry bush crops. The dormancy control system results in dramatic increases in crop yield, and may be coupled with orchard planting directional orientation, evaporative cooling systems, and chemical and hormone based spray applications

A dormancy control system for use with plants such as fruit trees, nut trees, and perennial berry bushes planted in commercial settings. The dormancy control system employs vertical shade structures to control the dormancy of plants such as fruit trees, nut trees, and perennial berry bush crops. The dormancy control system results in dramatic increases in crop yield, and may be coupled with orchard planting directional orientation, evaporative cooling systems, and chemical and hormone based spray applications

In some embodiments, a method for managing growing crops in a crop growing farm includes operating one or more unmanned aerial vehicles (UAV) to fly over a plurality of sections of a crop growing farm. The UAVs are fitted with a plurality of cameras equipped to generate images in a plurality of spectrums. The plurality of sections of the crop growing farm grow crops of one or more types or varietals. The method further includes taking a plurality of aerial images of the sections of the vineyard in the plurality of spectrums, using the plurality of cameras, while the UAVs are flying over the plurality of sections of the crop growing farm, and executing an analyzer on a computing system to machine analyze the plurality of aerial images for anomalies associated with growing the crops of the one or more types or varietals. The machine analysis takes into consideration topological information of the crop growing farm, as well as current planting information of the crop growing farm.

In some embodiments, a method for managing growing crops in a crop growing farm includes operating one or more unmanned aerial vehicles (UAV) to fly over a plurality of sections of a crop growing farm. The UAVs are fitted with a plurality of cameras equipped to generate images in a plurality of spectrums. The plurality of sections of the crop growing farm grow crops of one or more types or varietals. The method further includes taking a plurality of aerial images of the sections of the vineyard in the plurality of spectrums, using the plurality of cameras, while the UAVs are flying over the plurality of sections of the crop growing farm, and executing an analyzer on a computing system to machine analyze the plurality of aerial images for anomalies associated with growing the crops of the one or more types or varietals. The machine analysis takes into consideration topological information of the crop growing farm, as well as current planting information of the crop growing farm.

The invention relates to a method for performing data analysis for plant phenotyping of single plants in a field and a data acquisition and evaluation system for performing data analysis for plant phenotyping of single plants in a field. Further, the invention relates to a mobile platform for use in said method and/or in said data acquisition and evaluation system and a use of the mobile platform for said method and/or said data acquisition and evaluation system. The method comprises the steps of capturing spectral data via a hyperspectral imaging sensor, capturing image data via an image sensor, capturing georeference data via an inertial measurement unit, spatializing the image data to generate georeferenced image data and a digital surface model, spatializing the spectral data, generating georeferenced spectral data based on the spatialized spectral data and the digital surface model and overlaying the georeferenced image data and georeferenced spectral data with field plan information to generate a high-resolution analysis data set.

A plant monitoring apparatus 1 includes: an extraction unit 2 that extracts a feature amount in a frequency response of vibration of a target plant with use of the vibration; a calculation unit 3 that calculates a change that indicates growth of the plant, based on the extracted feature amount and a reference feature amount that corresponds to a reference state of the plant; and an estimation unit 4 that estimates a state of the plant by, with use of the calculated change, referencing state information in which changes of the feature amount from the reference feature amount corresponding to growth of the plant are associated with states of the plant.

A plant monitoring apparatus 100 includes: a soil state estimation unit 200 that estimates a state of soil by, with use of frequency association information calculated based on vibration measured via the soil, referencing soil state estimation information in which the frequency association information and information indicating states of the soil are associated with soil states; and a plant state estimation unit 300 that estimates a state of a plant by, with use of growth association information that was calculated based on vibration of the plant and indicates growth of the plant, referencing plant state estimation information in which the growth association information and information indicating states of the plant above a plant soil surface are associated with plant states above the plant soil surface.

In an example embodiment, a plant light reflection apparatus may comprise two opposing reflector panels configured to reflect light, wherein each reflector panel may comprise one or more pieces of reflection material. A reflector support apparatus may be configured to support the at least two opposing reflector panels, and may comprise a central section configured to accept one or more plants. The bottom major edges of the one or more pieces of reflection material of the two opposing reflector panels may be disposed in proximity to main stems of the one or more plants in the central section, and to reflect light thereon.