A framework for diagnosing and predicting a suitability of soil conditions to various agricultural operations is performed in a combined, multi-part approach for simulating relationships between predictive data and observable outcomes. The framework includes analyzing one or more factors relevant to field trafficability, workability, and suitability for agricultural operations due to the effects of freezing and thawing cycles, and developing artificial intelligence systems to learn relationships between datasets to produce improved indications of trafficability, workability, and forecasts of suitability windows for a particular user, user community, farm, farm group, field, or equipment. The framework also includes a real-time feedback mechanism by which a user can validate or correct these indications and forecasts. The framework may further be configured to override one or more of the soil state assessments to ensure that indicators and forecasts are consistent with the recently-provided feedback.

There is disclosed a method and system for regulating plant irrigation at a crop field. The method comprises obtaining soil water tension (SWT) data and/or soil water content (SWC) data corresponding to a crop field. The SWT data and/or the SWC data is segmented into three segments. A respective line of best fit is determined for each of the three segments. The intercepts of the lines of best fit are used to determine an irrigation start threshold and an irrigation stop threshold. Devices that control irrigation for the crop field are caused to start or stop irrigation based on the irrigation start threshold and irrigation stop threshold.

Novel tools and techniques are provided for implementing Internet of Things (“IoT”)-based microwell solution for irrigation. In various embodiments, in response to receiving, from the plurality of sensors, first sensor data indicative of environmental conditions within an area, a computing system may analyze the first sensor data to determine parameters associated with water requirements within the area, may generate a water distribution plan based at least in part on the determined parameters, and may map the generated water distribution plan to a positional map of a plurality of microwells disposed at pre-installed locations within the area. The computing system may generate and send instructions to the microwells to pump water from an underground water source(s) (in some cases, surface water sources as well) and to irrigate plants or crops in the area using integrated irrigation systems, based on the mapping. The microwells and sensors may utilize IoT functionalities.

Presented herein are systems and methods for automatically determining liquid coverage on plant surfaces. More particularly, in certain embodiments, presented herein is a system for receiving an image depicting one or more plant surfaces, automatically identifying the plant surfaces in the image and distinguishing portions covered by liquid, and automatically determining a liquid coverage value. In some embodiments, the system determines changes to liquid spraying parameters to achieve desired liquid coverage values.

Some embodiments provide a system and method for interfacing with an irrigation controller based on rainfall, the system comprising: an interface unit including a housing and a control unit within the housing and configured to: cause an interruption of one or more watering schedules executed by the irrigation controller, which is separate from the interface unit, based on signaling received from a rain sensor including hygroscopic material, when a sensed expansion of the hygroscopic material is above a set rainfall accumulation threshold parameter, the rain sensor being separate from the interface unit and the hygroscopic material being configured to expand in response to being contacted by the rainfall and to contract in response to an absence of the rainfall; and remove the interruption after a completion of a predetermined interval of time after a sensed contraction of the hygroscopic material indicative of a rainfall stop.

A system for intelligent irrigation based on moisture-level data acquired from multiple locations. The system includes a processor of an irrigation server connected to a moisture-level sensor and to a water tank control unit over a network; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a moisture-level data from the moisture-level sensor at a plant location; determine a plant type based on the plant location associated with the moisture-level sensor; process the moisture-level data and the plant type to generate a feature vector; provide the a feature vector to an AI module for generation of an irrigation instruction output; and responsive to the irrigation instruction output received from the AI module, send a command signal to the water tank control unit to turn on a pump for irrigation of the plant location.

Methods and apparatus are provided herein for sensing rain fall for use in irrigation control. In one embodiment, a wireless rain sensor comprises a housing at least partially covering a first sensor, a controller and a wireless transmitter. The first sensor comprises a moisture absorptive material located to be contacted by rain fall and configured to expand in response to the contact with the rain fall and contract in response to an absence of the rain fall. The controller is coupled to the first sensor and configured to output signals corresponding to a variable amount of expansion and contraction of the moisture absorptive material. The wireless transmitter is configured to transmit wireless signals, at least one wireless signal comprising data corresponding to the variable amount of expansion and contraction of the moisture absorptive material.

A method for managing an irrigation system by generating a multi-dimensional model of an environment of the irrigation system; determining irrigation system control options based on the model, a current state of the irrigation system and the environment of the irrigation system; analyzing water spray pattern, wind speed and weather parameters, and beamforming water spray to reach edges of the spray pattern to water a predetermined area; with a drone, inspecting plants or crops for a problem; and controlling the irrigation system to respond to the problem.

Systems and methods for providing irrigation water to a soil depth of a crop rootzone in a plurality of crop fields using a sensor network and soil moisture modeling are provided. In various embodiments methods include receiving data from a sensor network in a first crop field and determining a soil moisture model using data from the sensor network in the first field. Various embodiments further include determining a first field irrigation time using the soil moisture model, the first field irrigation time providing irrigation water to a soil depth of the crop rootzone above a Wilting Point (WP) and below a Field Capacity (FC) of soil in the first field, and applying the soil moisture model to a second field.

Methods and apparatus are provided herein for sensing rain fall for use in irrigation control. In one embodiment, a wireless rain sensor comprises a housing at least partially covering a first sensor, a controller and a wireless transmitter. The first sensor comprises a moisture absorptive material located to be contacted by rain fall and configured to expand in response to the contact with the rain fall and contract in response to an absence of the rain fall. The controller is coupled to the first sensor and configured to output signals corresponding to a variable amount of expansion and contraction of the moisture absorptive material. The wireless transmitter is configured to transmit wireless signals, at least one wireless signal comprising data corresponding to the variable amount of expansion and contraction of the moisture absorptive material.