Systems, methods, and apparatus for large scale, high throughput phenotyping of items including plants to derive environment/genetics correlations based on controlled variation of environment. Spatial and temporal resolution is improved by an integrated system that can use space efficiently yet concurrently process a large number of replicates but in varying environments. This is accomplished by using an array of miniature greenhouses, each in communication with one or more generators or regulators of an environmental factor that can be independently supplied and controlled to each miniature greenhouse. The controller also controls acquisition of data from each greenhouse as well as processing of that data into phenomic/genomic correlations.

The disclosure relates to methods for mapping temporal and spatial stability and sustainability of a cropping system. In some methods, remote sensing imagery over multiple time series elements (such as for past growing seasons) is used to characterize small-scale field stability and variability relative to larger surrounding land areas. In some methods, remote sensing imagery over multiple time series elements (such as for the growing season) is used to characterize small-scale field stability and variability relative to larger surrounding land areas. In some methods, a crop model is used to determine dependent cropping system parameters related to agricultural sustainability, which can be used to characterize small-scale field sustainability scores for such parameters relative to larger surrounding land areas. Such stability and sustainability maps can inform crop management activities for fields in the larger land areas and on the smaller scales, for example using crop models to determine such crop management activities to improve crop productivity, improve economic productivity, and/or reduce adverse environmental impact for the field as a whole and/or sub-regions thereof.

A plant monitoring system includes one or more internet of things (IOT) sensors to detect conditions associated with a plant; a database coupled to the sensors; and a chatbot coupled to the database to answer queries from a user about the plant condition.

A plant monitoring system includes one or more internet of things (IOT) sensors to detect conditions associated with a plant; a database coupled to the sensors; and a chatbot coupled to the database to answer queries from a user about the plant condition.

A method of determining health of a plant which method includes displaying, on a screen of an electronic display of a mobile computing device, a digital leaf colour chart with colours ranging from yellow green to dark green; displaying on the screen next to the digital leaf colour chart a white area on which a leaf of the plant is to be placed; receiving input to the mobile computing device from a user about which colour of the digital leaf colour chart that best matches the leaf placed on the white area; and the mobile computing device determining the plant health based on the received input.

A method of determining health of a plant which method includes displaying, on a screen of an electronic display of a mobile computing device, a digital leaf colour chart with colours ranging from yellow green to dark green; displaying on the screen next to the digital leaf colour chart a white area on which a leaf of the plant is to be placed; receiving input to the mobile computing device from a user about which colour of the digital leaf colour chart that best matches the leaf placed on the white area; and the mobile computing device determining the plant health based on the received input.

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 of selecting a treatment recommendation for nitrogen loss in a field includes measuring a first characteristic of a soil sample that is related to biologic nitrogen loss to produce a measured value. A second characteristic of the soil sample is measured and is used to select a sample group for the soil sample. The measured value is scaled based on the soil sample group the soil sample is placed into to form a scaled measure and the scaled measure is used to select a treatment recommendation.

A method of selecting a treatment recommendation for nitrogen loss in a field includes measuring a first characteristic of a soil sample that is related to biologic nitrogen loss to produce a measured value. A second characteristic of the soil sample is measured and is used to select a sample group for the soil sample. The measured value is scaled based on the soil sample group the soil sample is placed into to form a scaled measure and the scaled measure is used to select a treatment recommendation.