A system and method for evaluating soil characteristics. The system and method includes providing one or more soil test kits to a user. The soil tests kits may include ion-exchange resins and may instruct the user to collect a soil sample from his/her growing area, to combine the soil sample with the ion-exchange resins, and to provide the combination to the system for analysis. Other test kits may not include ion-exchange resins and may instruct the user to provide a soil sample from his/her growing area to the system for analysis. The system evaluates the ion-exchange resins and/or the soil samples to identify nutrient levels, pH levels, and other characteristics of the soil. Using the evaluation results, the system provides feedback, recommendations and/or products to the user to improve the soil conditions and to ensure a successful crop, yield, quality, and nutrient density. The system and method also may include providing a second soil test kit to the user at a predetermined time after the first, to evaluate a second soil sample, and to compare the second evaluation results with the first to assess the improvement of the soil conditions.

Methods and systems for crop management are disclosed. An example method can comprise receiving first information associated with an environmental management zone. The first information can relate to one or more of a land characteristic and a management practice. The first information can comprise a soil type of the environmental management zone. An example method can comprise, receiving historical weather data relating to the environmental management zone. An example method can comprise receiving real-time weather data relating to the environmental management zone. An example method can comprise executing a growth model to predict a nitrogen range for the environmental management zone based on one or more of the first information, the historical weather data, and the real-time weather data. The nitrogen range can comprise probabilities for one or more of a current time period and a future time period in the growing season.

In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

A system for managing the dosing of inputs to be applied to a surface includes an electronic device housed on board an agricultural machine and a remote management server. The electronic device includes a weather sensor for measuring a weather condition, a central unit for collecting a weather datum containing information relating to the measured weather condition, and—first communication means for transmitting the weather datum to the management server. The management server includes—a processor for processing the weather datum and usage-specific data so as to generate a dosing recommendation for the inputs on the basis of a predetermined and learning dosing model, and—second transmission means for transmitting the dosing recommendation to a communication terminal.

Various embodiments relate 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, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

An automated computer-controlled sampling system and related methods for collecting, processing, and analyzing agricultural samples for various chemical properties such as plant available nutrients. The sampling system allows multiple samples to be processed and analyzed for different analytes or chemical properties in a simultaneous concurrent or semi-concurrent manner. Advantageously, the system can process soil samples in the “as collected” condition without drying or grinding. The system generally includes a sample preparation sub-system which receives soil samples collected by a probe collection sub-system and produces a slurry (i.e. mixture of soil, vegetation, and/or manure and water), and a chemical analysis sub-system which processes the prepared slurry samples for quantifying multiple analytes and/or chemical properties of the sample. The sample preparation and chemical analysis sub-systems can be used to analyze soil, vegetation, and/or manure samples. A soil collection system is disclosed which captures and directs samples to the sampling system for processing.

One embodiment provides a method, including: obtaining information related to farming activities of a farmer; predicting an annotation category for the information, wherein the annotation category identifies a topic of the information; selecting an annotator for annotating the information based upon the annotation category, wherein the selecting comprises utilizing (i) a social proximity constraint identifying a social connection between the farmer and another farmer and (ii) a farm signature constraint identifying a similarity of the farmer to another farmer; assigning the annotator to annotate the obtained information; and receiving annotations for the information.

Various embodiments relate 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, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

An automated computer-controlled sampling system and related methods for collecting, processing, and analyzing agricultural samples for various chemical properties such as plant available nutrients. The sampling system allows multiple samples to be processed and analyzed for different analytes or chemical properties in a simultaneous concurrent or semi-concurrent manner. Advantageously, the system can process soil samples in the “as collected” condition without drying or grinding. The system generally includes a sample preparation sub-system which receives soil samples collected by a probe collection sub-system and produces a slurry (i.e. mixture of soil, vegetation, and/or manure and water), and a chemical analysis sub-system which processes the prepared slurry samples for quantifying multiple analytes and/or chemical properties of the sample. The sample preparation and chemical analysis sub-systems can be used to analyze soil, vegetation, and/or manure samples. A soil collection system is disclosed which captures and directs samples to the sampling system for processing.

Embodiments of the invention provide an automated plant probe system and method. The plant probe can include a body and a housing with a hardware module. The hardware module can include a communication module, an electronic controller, and memory. The plant probe can include a probe with a sensor module. The sensor module can including various sensors, such as a moisture sensor and/or a growing media sensor. The plant probe system can include a control system in communication with the communication module of the plant probe. The control system can receive plant data from the sensor module and use the plant data to provide plant recommendations.