A sensing system contains a vibration device attached to a stem of an agricultural crop for applying vibration to the agricultural crop, at least one sensor attached to the stem of the agricultural crop for sensing vibration of the agricultural crop caused by the vibration applied to the agricultural product from the vibration device to transmit vibration information relate to the vibration of the agricultural crop and a computing device for identifying one local maximum value among a plurality of local maximum values in a frequency spectrum obtained from the vibration information received from the at least one sensor as a resonance frequency of the vibration of the agricultural crop to determine a growth degree of a fruit of the agricultural crop based on the identified resonance frequency

A sensing system contains a vibration device attached to a stem of an agricultural crop for applying vibration to the agricultural crop, at least one sensor attached to the stem of the agricultural crop for sensing vibration of the agricultural crop caused by the vibration applied to the agricultural product from the vibration device to transmit vibration information relate to the vibration of the agricultural crop and a computing device for identifying one local maximum value among a plurality of local maximum values in a frequency spectrum obtained from the vibration information received from the at least one sensor as a resonance frequency of the vibration of the agricultural crop to determine a growth degree of a fruit of the agricultural crop based on the identified resonance frequency

A method for planting marine grass seed via a seed assembly comprising the steps of: selecting a body of water and area suitable for eele grass and shellfish, wherein the body of water has a bottom wherein shellfish can sufficiently bury themselves; selecting a living shellfish; placing a drop of non-toxic biodegradable adhesive on the shell of the living shellfish; positioning a grass seed to touch adhesive; waiting a predetermined time period for the adhesive to cure; and deploying the seed assembly into the body of water, wherein the seed assembly comprises the living shellfish, the drop of adhesive, and the positioned grass seed, wherein the body of water has a bottom where the shellfish can bury themselves sufficiently to bury the grass seed.

One variation of a method for interpreting pressures in plants includes: accessing a first image of a first set of sentinel plants in a field; accessing a second image of a second set of sentinel plants in the field, recorded during a first period; interpreting a first pressure of a stressor in the first set based on features extracted from the first image, captured during the first period; interpreting a second pressure in the second set based on features extracted from the second image; deriving a model associating pressure at the first set and pressure at the second set based on the first pressure and the second pressure; interpreting a third pressure in the first set based on features extracted from a third image captured during a second period; and predicting a fourth pressure in the second set during the second period based on the third pressure and the model.

A multi-sensor device comprises a housing containing multiple sensor modules for capturing and transmitting sensor data for plants in a crop. A control unit within the housing is operable to control the sensor modules, and a communications interface is connected to the control unit for transmitting data from said plurality of sensor modules. The sensor modules can include a physiological sensor, a surface analysis sensor, and chemical sensor. The multi-sensor device can be used as a hand-held device or mounted to a mobile platform for use in an automated crop monitoring system.

A multi-sensor device comprises a housing containing multiple sensor modules for capturing and transmitting sensor data for plants in a crop. A control unit within the housing is operable to control the sensor modules, and a communications interface is connected to the control unit for transmitting data from said plurality of sensor modules. The sensor modules can include a physiological sensor, a surface analysis sensor, and chemical sensor. The multi-sensor device can be used as a hand-held device or mounted to a mobile platform for use in an automated crop monitoring system.

The present invention provides media, kits, systems, and methods for achieving large scale pistachio production within a short time via bioculture, large scale yam production within a short time via bioculture, high multiplication rate of plants including cannabis via in vitro micropropagation, high induction rates of somatic embryos from later buds in bamboo, reduced production of phenolic compounds in plants, high production of virus-free plants, including potato, and large scale hemp production via culturing. The present invention for pistachio and yam production results in shorter tuber development phase and higher yield. In some embodiments, the present invention provides compositions, methods, and systems for the micropropagation and mass production of perennials, grasses, bamboos, cannabis and phyto-pharmaceutical plants as well as hemp plants. In some embodiments, the present invention provides compositions, methods, and systems for reducing the production of a phenolic by a plant, such as bamboo.

A precision agriculture support system is provided with a measuring device, a storage device and a plant species determining unit. The measuring device measures a first spectral characteristic of light derived from vegetation in a support target area. The storage device stores a database of spectrum according to species that shows a spectral characteristic of a desired crop. The plant species determining unit determines whether a plant included in the vegetation is the desired crop or not based on the database of spectrum according to species and a measurement result of the first spectral characteristic. The plant species determination unit further carries out distinction of agricultural crops, distinction of agricultural crops and weeds and the like. Furthermore, the precision agriculture support system identifies an area where abnormality is occurring, estimates a nature of the abnormality and carries out an early warning by providing a countermeasure against the abnormality.

A precision agriculture support system is provided with a measuring device, a storage device and a plant species determining unit. The measuring device measures a first spectral characteristic of light derived from vegetation in a support target area. The storage device stores a database of spectrum according to species that shows a spectral characteristic of a desired crop. The plant species determining unit determines whether a plant included in the vegetation is the desired crop or not based on the database of spectrum according to species and a measurement result of the first spectral characteristic. The plant species determination unit further carries out distinction of agricultural crops, distinction of agricultural crops and weeds and the like. Furthermore, the precision agriculture support system identifies an area where abnormality is occurring, estimates a nature of the abnormality and carries out an early warning by providing a countermeasure against the abnormality.

A device includes an image data receiving component, a vegetation index generation component, a crop data receiving component, a masking component and a multivariate regression component. The image data receiving component receives image data of a geographic region. The vegetation index generation component generates an array of vegetation indices based on the received image data, and includes a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data. The crop data receiving component receives crop data associated with the geographic region. The masking component generates a masked vegetation index based on the array of vegetation indices and the received crop data. The multivariate regression component generates a crop parameter based on the masked vegetation index.