In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value.

Disclosed herein is an apparatus for facilitating managing cultivation of crops based on monitoring the crops. Further, the apparatus comprises an apparatus body, cameras, light sensors, a processing unit, and a communication interface. Further, the cameras generate a measurement of a crop and a field portion. Further, the light sensors generate an environment measurement of an environment of the apparatus. Further, the processing unit analyzes the environment measurement, determines a factor affecting the measurement, and generates a calibrating factor for the cameras. Further, the calibrating factor facilitates compensating the affecting of the factor in the measurement. Further, the cameras calibrate a camera parameter of the cameras based on the calibrating factor to generate the measurement. Further, the processing unit analyzes the measurement and generates a status of the crop. Further, the communication interface transmits the status to a device.

A map generator generates a replanting map designating a particular area in a field in which it is recommended to add additional seeds. A function of the agricultural machine is then controlled based at least in part on the replanting map so as to facilitate planting additional seeds in the designated particular area.

Systems and methods automate the design and execution of randomized experiments. Portions of a field are planted using an agricultural vehicle configured to randomly vary planting parameters when planting a portion of the field. A resulting crop outcome across each portion or sub-portion of the field is observed. A training set of data is generated that includes the varied planting parameters and the associated crop outcomes for each portion of the field. A machine-learned model is trained using the training set of data and is configured to predict a crop outcome for a portion of the field based on historical and forecast conditions and a set of planting parameters applied to a portion of the field. For subsequent iterations, for a target portion of the field, the machine-learned model can be applied to identify a set of planting parameters for planting the target portion of the field to optimize a desired crop outcome.

A system for collecting and chemically analyzing water samples extracted from the soil to measure one or more analytes of interest such as soil nutrient levels of agricultural interest for increasing crop yield and quality in one use of the system. The system includes a sample collection probe (20) comprising a filter media (26) arranged to contact the soil when embedded therein and capture a water sample from the soil, and operably coupled to a sample processing sub-system (180) thereby collectively forming a sampling station (190). The sub-system (180) is configured to receive and analyze the water sample. A programmable probe controller (60) directs operation of the sample collection, processing, and chemical analysis in situ. A networked array (110) of sampling probes dispersed throughout the field may communicate wirelessly with at least one remote electronic device such as via the cloud computing (102). A modular version of a sampling probe (200) permits customized sampling at various soil depths.

A system and method to distribute pesticides, fertilizers, water, and other materials on a farm with accuracy and precision is disclosed in order to combat the problems imposed on the environment due to over-fertilization and over use of pesticides. This system and method is a social networking control system in which multiple farms have independent grids of sensors capable of detecting the presence of pesticides, fertilizers, water, and other materials in the air, in the top-soil, and in the groundwater. These grids of sensors detect the location and concentration of these materials and reports them back to a social control system for analysis. The control system regulates the deposition of further chemicals through computer control of the chemical dispersal systems.

A method of managing crops using an electronic device having an interface. Inputs of crop data is received, and each of the crop data is associated with a sample site location corresponding to each of a plurality of images captured by an image capturing device. A graph plotting one or more types of crop data including data associated with the plurality of images is generated in a first display region of the interface. A subset of sample site locations requiring one of a predetermined set of actions is displayed on the map in a second display region of the interface based on a selection within one of the plots on the graph.

An agricultural system comprising includes a support assembly having one or more support structures and one or more propulsion units coupled to the one or more support structures. The agricultural system includes one or more actuatable work tool assemblies having one or more measurement attachments configured to perform one or more measurements of at least one of one or more objects or one or more regions within an environment. The one or more actuatable work tool assemblies may be actuated by one or more actuation systems. The agricultural system may include a controller configured to cause one or more processors to direct the one or more actuation systems to actuate the one or more actuatable work tool assemblies position to perform one or more measurements of at least one of one or more objects or one or more regions within the environment.

The present disclosure relates to the field of a soil analysis apparatus. The apparatus comprises an enclosure, a provision for introducing a soil solution to be analyse, reservoir, a plurality of storage containers to store reagent solution, a frame member having a plurality of apertures to support a plurality of dispensing pipes, at least one pump coupled to a control unit and in fluid communication with the storage containers and the reservoir to dispense a predetermined quantity of the reagent and the soil solution into a receptacle. Further, at least one robotic arm assembly coupled with a control unit, traverses within the enclosure to receive the soil solution and reagents solution and to perform a mixing operation to obtain a mixture of soil solution and reagent solution. Further, an image capturing unit is present to capture images of the mixture to analyse the soil properties and nutrient content.

The embodiments disclose a method comprising creating 3D models of an orchard with multiple plants in the form of a densified point cloud using oblique aerial RGB imaging and photogrammetry, identifying and segmenting individual plants of the orchard from the 3D models, simulating sunlight radiation in the 3D models, determining a shading effect of branches and neighboring plants on each individual plant at any time of the day, determining canopy light interception of each plant, analyzing canopy geometry of each plant in the 3D models, forecasting potential yield of each plant based on the measured canopy light interception and calculating nitrogen and water requirements of each plant based on the potential yield and other predetermined field, environmental and climate factors and validating the yield forecasting model using the canopy light interception data by measuring the actual yield for each plant.