A plant biosensor includes a solar radiation sensor that measures a solar radiation amount with which the plant is irradiated, a sap flow sensor that measures a flow rate of sap flowing in a body of the plant, and an absorbed nutrient sensor that measures a nutritional state of the plant.

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

The present invention provides a system for identifying individual crowns of individual fruit trees using aerial images. A crown identification device 40 of the present invention includes an identification criterion determination unit 41 and a crown identification unit 42. The identification criterion determination unit 41 includes a first image acquisition section 411 that acquires a first aerial image including a plurality of individual fruit trees in a deciduous period in a fruit farm field, a skeleton extraction section 412 that processes the first aerial image to extract a whole crown skeleton including the plurality of individual fruit trees, a vertex extraction unit 413 that extracts vertexes of each crown skeleton corresponding to each individual fruit tree, and an identification criterion extraction section 414 that extracts a crown candidate region of a minimum polygonal shape including all the vertexes as an identification criterion for each individual fruit tree and extracts a centroid of the crown candidate region. The crown identification unit 42 includes a second image acquisition section 421 that acquires a second aerial image of the fruit tree farm field at the time of identifying a crown at the same scale as the first aerial image, a whole crown extraction section 422 that processes the second aerial image to extract a whole crown image including the plurality of individual fruit trees, and a crown identification section 423 that collates the crown candidate region and the centroid of the identification criterion with the whole crown image to identify a crown region of each individual fruit tree in the second aerial image.

The present invention provides a system for identifying individual crowns of individual fruit trees using aerial images. A crown identification device 40 of the present invention includes an identification criterion determination unit 41 and a crown identification unit 42. The identification criterion determination unit 41 includes a first image acquisition section 411 that acquires a first aerial image including a plurality of individual fruit trees in a deciduous period in a fruit farm field, a skeleton extraction section 412 that processes the first aerial image to extract a whole crown skeleton including the plurality of individual fruit trees, a vertex extraction unit 413 that extracts vertexes of each crown skeleton corresponding to each individual fruit tree, and an identification criterion extraction section 414 that extracts a crown candidate region of a minimum polygonal shape including all the vertexes as an identification criterion for each individual fruit tree and extracts a centroid of the crown candidate region. The crown identification unit 42 includes a second image acquisition section 421 that acquires a second aerial image of the fruit tree farm field at the time of identifying a crown at the same scale as the first aerial image, a whole crown extraction section 422 that processes the second aerial image to extract a whole crown image including the plurality of individual fruit trees, and a crown identification section 423 that collates the crown candidate region and the centroid of the identification criterion with the whole crown image to identify a crown region of each individual fruit tree in the second aerial image.

Described herein are techniques for generating contextually rich plant images. A number of data captures of raw plant data are generated via a sensing unit configured to navigate a growing facility. Metadata is generated and assigned to the raw plant data including at least one of: plant location, timestamp, plant identification, plant strain, facility identification, facility location, facility type, health risk factors, plant conditions, and human observations. Images generated by the sensing unit are analyzed and pixel annotations are generated in the images based on their relation to one or more plant well-being features. Data tags are generated and assigned the data captures based on an analysis of the data captures. The data tags are text phrases linking a particular data capture to a specific threat to plant well-being.

An information processing apparatus calculates an accurate index for the growth state of a plant without increasing a user load. The apparatus includes a receiver that receives an input of a captured image obtained by capturing an image of a plant cultivation area including a rectangular area in a longitudinal direction of the rectangular area, a conversion factor calculator that calculates conversion factors at positions in a depth direction in the captured image by determining a pixel count in a width direction of the rectangular area in the captured image input into the receiver at each position in the depth direction and dividing an actual width of the rectangular area by the determined pixel count, and an index calculator that calculates an index for a growth state of the plant by analyzing the captured image using the conversion factors at the positions calculated by the conversion factor calculator.

Embodiments of the present invention are directed to lightweight, portable spectrograph systems configured for applications in high-throughput crop phenotyping and plant health assessment, and associated methods.

A plant growth monitoring system comprises an image capture system for capturing images over time of a plant being monitored. The images are used to derive successive time instants corresponding to predetermined growth states of the plant. A temperature exposure parameter is obtained between the successive time instants, based on a monitored temperature vs. time function. Information relating to a plant health is derived from the temperature exposure parameter.

A system for estimating a harvesting time in a plant growing environment is configured to determine a light setting used by one or more light sources (11) to illuminate a section (45) of the plant growing environment with horticulture grow light. The light setting comprises at least a color component associated with the section of the plant growing environment. The system is further configured to obtain light measurement information from one or more color sensors (21) when the one or more color sensors are co-located with the one or more light sources. The system is configured to estimate a harvesting time for the section of the plant growing environment based on the light measurement information, an intensity of the color component in the light setting and a cultivation temperature protocol for the section of the plant growing environment. The cultivation temperature protocol comprises one or more assumed cultivation temperatures.