The present invention relates to a method for treatment of an agricultural field, the method comprising the steps: 1) receiving (S10) a parametrization (10) for controlling a treatment device (200) by the treatment device (200) from a field manager system (100); 2) receiving (S20) from at least one soil sensor (400) real-time soil information on the real-world situation of the geographical location G1 in the agricultural field; 3) processing (S30) the real-time soil information to generate processed information (30), 4) determining (S40) a control signal (50) for controlling a treatment arrangement (270) of the treatment device (200) based on the received parametrization (10) and the processed information (30), 5) executing (S50) a treatment on the geographical location G2 in the agricultural field, wherein the treatment is executed based on the control signal (50) real-time after receiving the real-time soil information in such a way that the distance between location G1 and location G2 does not exceed 100 meters.

Embodiments relate to a system that generates a crop yield component map for a field. The system determines amounts of nitrogen applied to each portion of the field by a set of nitrogen applicator farming machines. The system accesses crop yield data associated with a crop that was grown in the field. The crop yield data was generated by a set of harvester farming machines that travelled through the field and harvested plant parts of the crop. The system determines, by analyzing the crop yield data, plant part metrics for the harvested plant parts in each field portion. The system generates a crop yield component map that maps, for each field portion, a plant part metric associated with the field portion and an amount of nitrogen applied to the field portion. The component map may then be provided for display.

A mobile agricultural machine includes a row unit having a furrow opener mounted to the row unit and configured to engage a surface of ground over which the mobile agricultural machine travels to open a furrow in the ground. A furrow closer is mounted to the row unit behind the furrow opener and configured to engage the surface of the ground to close the furrow. A furrow sensor system is mounted to the row unit and configured to sense characteristics relative to the furrow opened by the furrow opener and generate a sensor signal indicative of the characteristics. The mobile agricultural machine can further include a control system configured to determine a furrow quality metric corresponding to the furrow sensed by the furrow sensor system based on the sensor signal and generate an action signal to control an action of the mobile agricultural machine based on the furrow quality metric.

A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

The disclosure relates generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects, causing one or more cartridges to be charged based on the subset of payloads, and, and implementing one or more cartridges at an agricultural projectile delivery system.

A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a stability metric.

The present invention relates to the use of a Raman spectral signature of nitrate, as a biomarker for an early, real-time diagnosis of nitrogen status in growing plants in a non-invasive or non-destructive way in order to detect nitrogen deficiency before the onset of any visible symptoms. The early, real-time diagnosis of nitrogen deficiency in plants makes it possible to correct nitrogen deficiency for the avoidance of negative effects on the yield and biomass of growing plants or leafy vegetables.

In some embodiments, the present disclosure pertains to methods and systems for optimizing a property of a plant. In some embodiments, the methods of the present disclosure include: (1) receiving one or more growth-related parameters of the plant; (2) receiving one or more phenotype-related parameters of the plant; (3) receiving one or more qualitative parameters of the plant; (4) utilizing a computing unit to evaluate said one or more growth-related parameters, phenotype-related parameters and qualitative parameters of the plant; and (5) utilizing the computing unit to adjust the one or more of the growth-related parameters of the plant based on the evaluation. In some embodiments, the aforementioned steps are repeated a plurality of times. In some embodiments, the parameters are stored in a database and utilized by a machine-learning algorithm to optimize the evaluation of the parameters and adjustment of the growth-related parameters.

The present invention discloses an agricultural implement for applying fertilizer beneath the surface of the soil. The implement has a main frame for connecting the agricultural implement to agricultural equipment. A swing arm is pivotally connected to the main frame. An adjustable actuator interconnects the swing arm to the main frame to control the movement of the swing arm with respect to the main frame. A pair of discs are mounted to the free end of the swing arm for rotation with respect to the swing arm. The discs are offset mounted with respect to one another and are angled outwardly with respect to the direction of travel of the implement. The discs form grooves for receipt of fertilizer. Fertilizer tubes are mounted to the swing arms adjacent the discs. The fertilizer tubes deposit fertilizer in the grooves formed by the discs.

Provided herein are methods, systems, and media that implement machine learning algorithms to determine a cultivar regimen recommendation for a crop based on crop yield and cultivar condition data.