An apparatus includes at least one processor configured to obtain multiple spatiotemporal population projection models. Different spatiotemporal population projection models are associated with different pests, diseases, or biocontrol agents. Each spatiotemporal population projection model defines how the associated pest, disease, or biocontrol agent spreads and contracts in a growing area over time. The at least one processor is also configured to receive information associated with an actual presence of a specific pest, disease, or biocontrol agent at one or more monitored spatial locations in the growing area. Different monitored spatial locations in the growing area are associated with different plants. The at least one processor is further configured to project a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent.

Implementations are described herein for predicting soil organic carbon (“SOC”) content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.

The present invention relates to the technical field of field crop cultivation, more particularly to a training method, an evaluation method, an electronic device and a storage medium. According to the present invention, a multispectral three-dimensional point cloud map is obtained through depth information and multispectral information, and the multispectral three-dimensional point cloud map is analyzed by utilizing an FVNet three-dimensional target detection algorithm, thereby acquiring crop feature information. Thus, more comprehensive crop information can be obtained, and a crop state evaluation model constructed based on an artificial neural network can be further trained with the crop feature information.

An autonomous farming machine navigable in an environment for performing farming action(s) is disclosed. The farming machine receives a notification from a manager that there are no obstacles in the blind spots of the detection system. The farming machine applies an obstacle detection model to the captured images to verify that there are no obstacles in unobstructed views. The farming machine determines a configuration of the farming machine. The farming machine determines a virtual safety bubble for the farming machine to autonomously perform the farming action(s) based on the determined configuration. The farming machine detects an obstacle in the environment by applying the obstacle detection model to the captured images. The farming machine determines that the obstacle is entering the virtual safety bubble. In response to determining that the obstacle is entering the virtual safety bubble, the farming machine terminates operation of the farming machine and/or enacts preventive measures.

Described herein are systems and method for monitoring and controlling field operations including planting and harvesting operations. In one embodiment, a data processing system of a machine includes a data transfer and processing module that communicates bi-directionally with different types of controllers and sensors mounted on the machine or an implement attached to the machine. The data transfer and processing module is configured to execute instructions to receive signals from these controllers and sensors, process these signals, and generate data for monitoring and controlling field operations of the machine. At least one display device is coupled to the data transfer and processing module. The at least one display device displays the data for monitoring and controlling field operations of the machine or implement to a user or operator.

In one aspect, a system for controlling an operation of agricultural implements may include a work vehicle configured to tow an implement. The work vehicle may include a hitch assembly having a draw point configured to be coupled to the implement. The work vehicle may further include an actuator configured to move the draw point relative to the hitch frame to adjust the position of the implement relative to the work vehicle. The implement may include a sensor configured to detect an operational parameter indicative of the operation of the implement. Additionally, the implement may further include a controller communicatively coupled to the sensor, with the controller being configured to initiate control of an operation of the actuator based on sensor data received from the sensor to adjust the operational parameter of the implement.

A system and method for optimal allocation of agricultural water based on water consumption control are provided. The system includes a data management module, configured to store and manage data related to water allocation; a basic data statistics module, configured to take statistics on various stored data; an agricultural evapotranspiration (ET) calculation module, configured to calculate an agricultural ET based on the stored data and statistical data; and a water resource allocation module, configured to generate a corresponding water resource allocation scheme based on an agricultural ET of each water consumption unit and corresponding data. Surface water and groundwater are jointly allocated to reduce groundwater exploitation, increase groundwater recharge, reduce invalid water loss and consumption in a carrying and allocation process, and improve an output benefit of agricultural water supply. This provides technical support for realization of agricultural target ETs in different hydrologic years and sustainable utilization of water resources.

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.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

A device detects a trigger to dispense a product at a using a tool operably coupled to a tractor. The device determines whether the tractor is in an automated mode, the automated mode enabling autonomous speed and direction navigation of the tractor. Responsive to determining that the tractor is not in the automated mode, the device determines whether the tool is in a ready state, and responsive to determining that the tool is in the ready state, commands the tool to dispense the product, wherein the tool is not commanded to dispense the product until the tool is in the ready state. Responsive to determining that the tractor is in the automated mode, the device commands the tool to dispense the product without determining whether the tool is in the ready state.