A crop detection system and method of using the same includes a machine vision system mounted to a mobile vehicle. The machine vision system includes an information capturing device connected to a computer having a processor and memory. The memory includes stored crop and field information. Positioning members are mounted to an extend forward of the mobile structure. The information capturing device includes a camera, a sensor, a transceiver and/or a stereo sensor configuration and is positioned to sense the presence, size, location and orientation of characteristics of a crop.

An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.

Methods and systems for controlling robotic actions for agricultural tasks are disclosed which use a spacing-aware plant detection model. A disclosed method, in which all steps are computer-implemented, includes receiving, using an imager moving along a crop row, at least one image of at least a portion of the crop row. The method also includes using the at least one image, a plant detection model, and an average inter-crop spacing for the crop row to generate an output from the plant detection model. The plant detection model is spacing aware in that the output of the plant detection model is altered or overridden based on the average inter-crop spacing. The method also includes outputting a control signal for the robotic action based on the output from the biased plant detection model. The method also includes conducting the robotic action for the agricultural task in response to the control signal.

Embodiments of the present disclosure relate generally to generating and utilizing three-dimensional terrain maps for vehicular control. Other embodiments may be described and/or claimed.

Systems and methods for agricultural path planning are described. For example, a method includes accessing a boundary data structure that encodes a polygon on a map; generating a set of parallel line segments of a path for a vehicle on the map inside of the polygon; generating a first line segment of the path connecting an ending-point of one of the line segments of the set to a starting-point of another one of the line segments of the set; identifying a first point on the first line segment that is a maximum distance from the polygon; and splitting the first line segment into two line segments, having a starting-point matching a starting-point of the first line segment and an ending-point matching an ending-point of the first line segment, that connect at a second point on the polygon encoded by the boundary data structure that is closest to the first point.

Techniques are described for generating population counts for seed production plants based on one or more images obtained from a camera on an aerial vehicle including, but not limited to, UAVs. The image(s) is processed to identify the plurality of rows of seed production plants, classify each one of the identified rows as either male or female, and produce a count of the number of seed production plants in the male rows and produce a count of the number of seed production plants in the female rows. The seed production plants can be corn seed plants or any other type of seed production plant in which male and female seed production plants can be distinguished from one another based on one or more aerial images.

Process and system of analysis and management for agricultural production, which includes the stages of loading historical data, measuring and surveying present data, predicting meteorological and hydrological events, generating an agricultural handing protocol, and transmitting the agricultural handling protocol.

A computer implemented method for generating digital models of relative crop yield based on nitrate values in the soil is provided. In an embodiment, nitrate measurements from soil during a particular portion of a crop's development and corresponding crop yields are received by an agricultural intelligence computing system. Based, at least in part, on the nitrate measurements and corresponding crop yields, the system determines maximum yields for each location of a plurality of locations. The system then converts each crop yield value into a relative crop yield by dividing the crop yield value by the maximum crop yield for the location. Using the relative crop yields and the corresponding nitrate values in the soil, the system generates a digital model of relative crop yield as a function of nitrate in the soil during the particular portion of the crop's development. When the system receives nitrate measurements from soil in a particular field during the particular portion of a crop's development, the system computes a relative yield value for the particular field using the model of relative crop yield.

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.