Described herein are systems and devices for controlling and monitoring liquid applications of agricultural fields. In one embodiment, a flow device for controlling flow during an agricultural operation includes an offset ball valve having multiple openings that rotate in position to control flow of a liquid through the offset ball valve to an outlet passage. The flow device also includes a first passage that provides a first flow path from an inlet to at least one opening of the offset ball valve and second passage that provides a second flow path from the inlet to at least one opening of the offset ball valve.

Systems, methods and apparatus are provided for monitoring soil properties. An example control and monitoring system for an agricultural implement having a plurality of row units includes a reflectivity sensor, a camera, a processor, and a monitoring device. The reflectivity sensor generates a reflectivity signal related to reflectivity of soil worked by the row units. The camera captures images of a seed trench opened by an opening assembly of the row units. The camera is disposed on a shank extension forward of seeds being deposited in the seed trench by the row units. The processor is configured to calculate a statistical variation in the reflectivity signal and the monitoring device is configured to display one or more of the images captured by the camera and a representation of the statistical variation.

The complex topographic and climatic characteristics of a geographic area, or plot of land, are expressed by means of an index product (SRI) which allows to link such plot to the qualitative properties of a given agricultural product (P) or to a plant variety able to produce said agricultural product, for example a vine and a wine.

A system and method for intelligent soil sampling has for a novelty robotic system 100 that samples soil based on the generation of sampling points through advanced artificial intelligence algorithms. The robotic system 100 comprises a robotic platform 101 with sampling modules 103, 105 and 108, which communicates with a server 111, that consists a localization module 113 containing artificial intelligence algorithms based on satellite images from multiple spectral channels and/or images from high-resolution drone for a given parcel 301, generates zones and determines the coordinates of points as the best representatives of the zones to take place efficiently and quickly sampling the land. Intelligent sampling takes place through several steps where the sampling limits are defined, so a mask is placed on a given plot, after which a pixel matrix with vegetation indices is formed, which is then normalized and K-mean algorithm in different spatial resolutions is worked on with calculation of probability that each pixel 315, 316 belongs to one of the K zones, taking into account its environment with a different number of pixels, where each pixel 315, 316 is associated with changes in spatial resolutions 311, 312, 313, diagonally 314, associated with new values of affiliation probabilities and finally in step 317 a consensus is reached where the final zones are determined and the probability of affiliation of pixels 315, 316 to zones is estimated based on local histograms of matrix entities 311, 312 and 313.

A system to receive data representing agronomic responses based on randomized replicated treatments conducted in test plots of agronomic environments, aggregate the data representing the agronomic responses into subsets of the data representing the agronomic responses, each subset of the data representing the agronomic responses associated with one of a number of performance zones, receive characteristics associated with a portion of a field and determine that the portion of the field represents a particular performance zone of the number of performance zones based on the characteristics associated with the portion of the field, recommend a particularized treatment level for a crop located in the portion of the field based on the particular performance zone, and communicate the particularized treatment level to a machine, the particularized treatment level to be applied to the portion of the field by the machine to optimize an agronomic response based on the particular performance zone.

An agricultural nutrient applicator includes a container and a nutrient distribution assembly operably coupled to the container to deliver a nutrient from the container. A spectroscopic reflectance crop sense system is provided that includes an optical window. A presentation assembly is mounted to the agricultural nutrient applicator and is configured to position live plants in a field proximate the optical window of the spectroscopic reflectance crop sense system as the agricultural nutrient applicator moves. A controller is coupled to the spectroscopic reflectance crop sense system and the nutrient distribution assembly. The controller is configured to obtain, from the spectroscopic reflectance crop sense system, information indicative of a measured nutrient level in the live plants and determine a remedial nutrient amount based on the measured nutrient level and a target nutrient level. The controller controls the nutrient distribution assembly based on the remedial amount.

A mobile agricultural machine obtains an agricultural characteristic map indicative of agricultural characteristics of a field, wherein the agricultural characteristic map is based on data collected at or prior to a first time. The mobile agricultural machine obtains supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time. An agricultural characteristic confidence output, indicative of a confidence level in the agricultural characteristics indicated by the agricultural characteristic map, is generated based on the agricultural characteristic map and the supplemental data. In some examples, an action signal is generated to control an action of the mobile agricultural machine based on the agricultural characteristic confidence output.

In one embodiment, an agricultural sampling apparatus is provided. The apparatus comprising: a wave emitter; a wave transmitter configured to direct the waves from the wave emitter as a plurality of linewise waves to irradiate surface points of the agricultural sample; a dispersive element configured to receive waves arriving from the sample and deflect the arriving waves in at least two directions depending upon the wavelength of an arriving wave; and a detector configured with a plurality of detection elements disposed in at least two dimensions, the detector configured to convert the waves arriving from the dispersive element to a signal.

Disclosed are methods of measuring the quantity of active organic matter in a soil sample, as well as kits for performing such measurements.

An approach to the prediction of soil density and subsoil crop growth may be provided. The approach may include subsoil sensor which can monitor changes in soil pressure and moisture conditions. The sensor data can be sent to a computer module which can process the data using a machine learning model predicting the soil density around a subsoil crop and the yield of the subsoil crop. A soil maintenance plan can be generated from the soil density prediction and/or the crop yield prediction. The soil maintenance plan can be sent to soil management robots, which can execute the soil maintenance plan.