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.

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.

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.

Systems and methods for 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 determining residue coverage within a field may include receiving, with one or more computing devices, first and second images of the field. The first image may depict a portion of the field at a first time during a crop-growing period and the second image may depict the portion of the field at a second time during the crop-growing period, with the first and second times being different. Furthermore, the method may include generating, with the one or more computing devices, an estimated residue coverage map for the field based on the received first and second images. Additionally, the method may include generating, with the one or more computing devices, a prescription map for the field based on the estimated residue coverage map.

A method includes receiving sensor inputs including one or more images comprising one or more agricultural objects; continuously performing a pose estimation of the treatment system based on sensor inputs that are time synchronized and fused; identifying the one or more agricultural objects as target objects; tracking the one or more agricultural objects identified by the analyzing; controlling an orientation of the treatment mechanism according to the pose estimation for targeting the one or more agricultural objects; and activating the treatment mechanism to treat the one or more agricultural objects according to the orientation.

A method and system for evaluating and predicting a set of crop-associated features at an agriculture site, the method comprising: receiving a set of samples associated with the agriculture site; generating a sample dataset upon processing the set of samples with a set of sample processing operations; generating a set of microbiome-associated features upon performing a set of transformation operations upon the sample dataset; and returning an analysis characterizing the set of crop-associated features based upon the set of microbiome-associated features. The method can further include steps for executing an action for producing a desired outcome in relation to the agriculture site, with respect to a specific soil type and a specific crop, based upon the analysis.

A method and system for evaluating and predicting a set of crop-associated features at an agriculture site, the method comprising: receiving a set of samples associated with the agriculture site; generating a sample dataset upon processing the set of samples with a set of sample processing operations; generating a set of microbiome-associated features upon performing a set of transformation operations upon the sample dataset; and returning an analysis characterizing the set of crop-associated features based upon the set of microbiome-associated features. The method can further include steps for executing an action for producing a desired outcome in relation to the agriculture site, with respect to a specific soil type and a specific crop, based upon the analysis.

Engineered food to promote health and maintain homeostasis in a subject and methods of producing the food.

A system and method for managing nutrient concentrate in water-based solutions is disclosed. The method includes receiving nutrient information of water-based solution from a data measurement unit at real-time and receiving a set of desired parameters associated with the crops from one or more sources. The method further generating a set of nutrients recommendations corresponding to one or more additional nutrients required in the water-based solution for maximum crop yield and determining a set of dosing parameters to inject the one or more additional nutrients in the water-based solution based on the nutrient information, the set of desired parameters and the set of nutrients recommendations by using a nutrient management based AI model. Further, the method includes the nutrient information, the set of desired parameters, the set of nutrients recommendation and the set of dosing parameters on user interface screen of one or more electronic devices.