A solution for evaluating a medium using electrical signals is described. A plurality of electrical signals having different frequencies are transmitted through the medium and signal data corresponding to the electrical signals after having traveled through the medium is acquired. A complex impedance and a complex permittivity and/or complex conductivity can be calculated for the medium. A set of characteristics of the medium can be computed using mixing models and/or known information of the medium. A level of one or more attributes of the medium can be determined from the characteristics using nonparametric Bayesian inference. One particular application is directed to determining a nitrate level of soil.

Systems and methods are provided for classifying a microbiome at a geographic site where agricultural activity is, or will be, conducted in order to improve and/or promote agricultural productivity at the site. Machine learning and/or artificial intelligence classifier tools use DNA sequencing input data and environmental information to generate recommendations for customized soil and/or crop treatment compositions, irrigation practices, and/or other agricultural activity, to enhance plant health and crop productivity.

A system for measuring moisture in soil below the ground surface comprises at least one passive microwave sensor device configured to measure natural thermal emissions from the soil and output a data signal and a processing circuit operably coupled to the at least one passive microwave sensor wherein the processing circuit is configured to receive the data signal and compile a soil moisture profile. The system further comprises a wide-band antenna wherein the at least one passive microwave sensor is located therein and an elongate horizontal mounting frame extending between first and second ends wherein the first end is securable to a mobile agricultural device and wherein the wide-band antenna is secured to the second end so as to position the wide-band antenna at a distance above the ground surface.

A transmitter device (100) includes a housing (102). The transmitter device (100) has a cable (110). The cable (110) is at least partially housed within the housing (102) and electrically connected to a socket (130). Further, the transmitter device (100) has a sensor (106). The sensor (106) is electrically coupled with the cable (110). The transmitter device (100) further includes a cover (104). The cover (104) at least partially covers the sensor (106). The transmitter dev ice (100) is characterized in that the socket (130) is disposed outside the housing (102), and the socket (130) is flexibly coupled to the housing (102) through the cable (110). The housing (102) includes a channel (111) such that the cable (110) is slidably received within the channel (111).

In the present application, a method for assessing water shortage risk, device, computer device and storage medium are provided, wherein the method for assessing water shortage risk comprises: acquiring a volume of blue water flowing into soil of a to-be-assessed-area within a target time period, and a volume of retention water flowing into and retained in the soil of the to-be-assessed-area within the target time period; and determining a water shortage risk indicator of the to-be-assessed-area indicating a degree of water shortage risk of the to-be-assessed-area, according to a ratio of the volume of blue water to the volume of retention water. A higher ratio of the volume of blue water to the volume of retention water indicates poor ability of the soil's water to meet the demand for use and greater degree of the water shortage risk of the soil.

Disclosed are methods and systems for accurate modeling of the soil-water retention curve (SWRC) for any soil texture class and with varying amounts of soil organic matter. The disclosed method leverages near-visible infrared spectroscopy (vis-NIRS) to obtain rapid measurements at low soil-water potential that are used to model soil-water retention functions.

In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value.

An automatic detection and recovery device for residual agricultural mulch film, and a method of using said device, comprising a quadcopter, a wheeled robot (9), and a host computer (8); the quadcopter is provided with a controller (1), a near infrared water content analyzer, and a WiFi module that are used to measure water content in soil and communicate with the host computer; the wheeled robot (9) comprises a water sprinkling device (2), a soil grabbing device (3), a sifting device (4), a delivery device (5), a recognition device (6), and a sorting device (7).

Various embodiments may relate to a fluid transfer system. The fluid transfer system may include a chamber containing a fluid absorbing material in contact with a device. The fluid transfer system may also include a reservoir including rows of bendable support members, the reservoir connected to the chamber for transport of a fluid from the reservoir to the fluid absorbing material. The fluid transfer system may further include a mass including rows of protrusions configured to engage with the rows of bendable support members, the mass configured to be arranged in the reservoir such that forces acting on the mass include a gravitational force on the mass, a floating force exerted by the fluid on the mass, and a contact force exerted by the rows of bendable support members on the mass.

Disclosed are various embodiments for reinforcement learning-based irrigation control to maintain or increase a crop yield or reduce water use. A computing device may be configured to determine an optimal irrigation schedule for a crop planted in a field by applying reinforcement learning (RL), where, for a given state of a total soil moisture, the computing device performs an action, the action comprising waiting or irrigating crop. An immediate reward may be assigned to a state-action pair, the state-action pair comprising the given state of the total soil moisture and the action performed. The computing device may instruct an irrigation system to apply irrigation to at least one crop in accordance with the optimal irrigation schedule determined, where the optimal irrigation schedule includes an amount of water to be applied at a predetermined time.