Disclosed are methods of producing nutrient solutions having unique isotopic fingerprints; methods of producing traceable plants; and methods of identifying the source of a traceable plant that does not rely on expensive artificially separated isotopes. Plants grown with these nutrient solutions will have unique isotopic fingerprints that will be difficult or impossible to counterfeit.

One variation of a method for automating transfer of plants within an agricultural facility includes: dispatching a loader to autonomously deliver a first moduledefining a first array of plant slots at a first density and loaded with a first set of plants at a first growth stagefrom a first grow location within an agricultural facility to a transfer station within the agricultural facility; dispatching the loader to autonomously deliver a second moduledefining a second array of plant slots at a second density less than the first density and empty of plantsto the transfer station; recording a module-level optical scan of the first module; extracting a viability parameter of the first set of plants from features detected in the module-level optical scan; and if the viability parameter falls outside of a target viability range, rejecting transfer of the first set of plants from the first module.

One variation of a method for automating transfer of plants within an agricultural facility includes: dispatching a loader to autonomously deliver a first moduledefining a first array of plant slots at a first density and loaded with a first set of plants at a first growth stagefrom a first grow location within an agricultural facility to a transfer station within the agricultural facility; dispatching the loader to autonomously deliver a second moduledefining a second array of plant slots at a second density less than the first density and empty of plantsto the transfer station; recording a module-level optical scan of the first module; extracting a viability parameter of the first set of plants from features detected in the module-level optical scan; and if the viability parameter falls outside of a target viability range, rejecting transfer of the first set of plants from the first module.

A lighting system for plant cultivation comprises a light source, a light source driving unit that drives the light source, and a control unit that transmits a pulse signal to the light source driving unit. The control unit comprises a first pulse generating unit that generates a first pulse signal (S1) of a predetermined frequency, a second pulse generating unit that generates a second pulse signal (S2) of a frequency different from the predetermined frequency, and a pulse signal selecting unit that selects any one of the first pulse signal (S1) and the second pulse signal (S2) to transmit the selected one to the light source driving unit. The light source driving unit includes a frequency determining unit that determines whether the frequency of the pulse signal received from the control unit is the frequency of the first pulse signal (S1) or the frequency of the second pulse signal (S2), a first driving unit that, when receiving the first pulse signal (S1) from the control unit, converts the first pulse signal (S1) to a constant current, output to drive the light source, and a second driving unit that, when receiving the second pulse signal (S2) from the control unit, outputs a pulse with an original pulse waveform of the second pulse signal (S2) to drive the light source.

A lighting system for plant cultivation comprises a light source, a light source driving unit that drives the light source, and a control unit that transmits a pulse signal to the light source driving unit. The control unit comprises a first pulse generating unit that generates a first pulse signal (S1) of a predetermined frequency, a second pulse generating unit that generates a second pulse signal (S2) of a frequency different from the predetermined frequency, and a pulse signal selecting unit that selects any one of the first pulse signal (S1) and the second pulse signal (S2) to transmit the selected one to the light source driving unit. The light source driving unit includes a frequency determining unit that determines whether the frequency of the pulse signal received from the control unit is the frequency of the first pulse signal (S1) or the frequency of the second pulse signal (S2), a first driving unit that, when receiving the first pulse signal (S1) from the control unit, converts the first pulse signal (S1) to a constant current, output to drive the light source, and a second driving unit that, when receiving the second pulse signal (S2) from the control unit, outputs a pulse with an original pulse waveform of the second pulse signal (S2) to drive the light source.

A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Herein is described an electronic sensor and Artificial Intelligence (AI) system capable of optimizing agriculture processes in real-time. A number of wired or wireless optical, electrical, thermal, chemical, biological, or other sensors are deployed in a plant's locality, possibly for its full lifecycle. These sensors provide long-term, in vivo observations of both the plant's environment and the plant itself. A plant's interaction with light is measured by persistent, cloud-connected sensors, including a spectral reflectance sensor, Chlorophyll Fluorescence detector (ChFl), ChFl imager, and a near-IR imager. This sensor data enables the in-vivo characterization of a plant's health status and photosynthetic efficiency. A cloud-powered AI system uses machine learning algorithms to model the system, recommend agricultural process improvements, and identify abnormalities associated with plant disease or pests. As the invention gets enough sensor data across various environmental conditions, an ideal target process flow can be identified for a given plant genotype, phenotype, or other additional specificity.

This invention relates to methods and compositions for providing a benefit to a plant by associating the plant with a beneficial endophyte of the genus Streptomyces, including benefits to a plant derived from a seed or other plant element treated with said endophyte. For example, this invention provides purified endophytes, synthetic combinations comprising endophytes, and methods of making and using the same. In particular, this invention relates to compositions and methods of improving soybean and maize plants.

This invention relates to methods and compositions for providing a benefit to a plant by associating the plant with a beneficial endophyte of the genus Streptomyces, including benefits to a plant derived from a seed or other plant element treated with said endophyte. For example, this invention provides purified endophytes, synthetic combinations comprising endophytes, and methods of making and using the same. In particular, this invention relates to compositions and methods of improving soybean and maize plants.

A plant matter sensor comprising: one or more emitters configured to emit two or more vertically spaced signals toward a plant; and one or more receivers configured to receive two or more reflected signals from the plant.