Virtual agronomist



Virtual agronomist (eng. virtual [] agronomist, abbreviation virtual reality and the profession of agronomist) - the use of digital technologies in agriculture, as a substitute for human manual and mental labor. A virtual agronomist is built through programming, based on computer technology and programming the specialist’s skills into a digital format. Refers to Internet of Things technologies. Virtual agronomist - software agent, assistant, online specialist substitute for a person, which can perform tasks (or services) for the user based on information entered by the user, data on technologies of high professional skills in cultural and ornamental and industrial cultivation of agricultural crops, as well as information obtained from various Internet resources (weather, soil composition, agricultural characteristics, region and agricultural technology, automation complex and agronomy needs). This could include field management and monitoring, running data collection automation, machine vision for yield analysis, and automating growth in a vertical farm. Examples of this kind are the programs Trimble, cerdi [] isda-africa ([]), greenbar [], inra [].

Description
In the context of the use of professional abbreviation by a virtual agronomist in the cultivation of grains, vegetables and fruits, herbs and forage crops, three key ones are defined:

-A drone equipped with computer vision that regularly monitors fields

-Agricultural machinery controlled by unmanned vehicles

-Urban farming management platforms

Key areas of application of the virtual agronomist:

- field monitoring

- detection, location, recognition, identification, classification and analysis of weeds, plant diseases, insects and pests

- analysis and forecasting of the harvest, harvest dates

- resource management, soil analysis, calculation of water resources, fertilizers, weather conditions

- automation of control and management on a vertical farm by creating artificial conditions similar to natural ones

- big data analysis, machine learning, artificial intelligence

The professional format of intelligent personal digital farming services - "automated" and "smart" farms - are formed on the basis of mobile devices and application programming interfaces (API), and are distributed through mobile applications. At the same time, intelligent automated virtual agronomists are designed to perform specific tasks specified in the user instructions and embedded technical solutions.

Known implementations of virtual agronomist
Field monitoring The Taranis system receives information from data analysis obtained from surveillance sensors, weather data, high-resolution aerial photographs and is capable of identifying sectors of the field with slow plant growth, identifying plants damaged by insects that are not receiving enough nutrients, and identifying diseased plants. As a result, Taranis will offer solution options, calculate deadlines and options for action.
 * Taranis Israel program. Field monitoring

Platform for Agriculture The platform processes information obtained from remote sensing of land in the fields. The farmer receives real-time data on the damage to cereal crops by diseases or pests. Assesses the condition of plants, calculates the required amount of pesticides, optimal timing for treating problem areas and suggests preventive measures. The system independently collects data on weather conditions in a specific area, humidity, and meteorological situation, provides a graph of changes in soil moisture, creates a forecast for yield and growth dynamics based on data from previous seasons. · Health Change Maps and Notifications platform from Farmers Edge; · Field Manager application from Bayer; · Hummingbird Technologies platform. Recognition application interface. These platforms use data from satellites, ground monitoring, and meteorological information using algorithms to analyze them.
 * Watson Decision Platform for Agriculture USA
 * There are a number of other platforms in the world that can analyze information and provide recommendations for housekeeping:

Monitoring of plant diseases The plant disease diagnostic service allows you to diagnose 60 diseases. Contains a library of images, which has a convenient service for sorting images. As the number of downloaded images increases, disease diagnostic algorithms also improve. Processes photographs, based on which, identifies diseases, damage and disturbances in plant development. The service is able to identify weeds and provide data on the nitrogen supply of the plant. Equipped with functions for sending notifications when a dangerous disease or pest is detected near plants.
 * Plantix app from Peat.
 * Scouting app on the Xarvio digital platform.

Urban Farm Management
 * Digital urban farming platform ACA Mi Campo Israel. Uses AI algorithms to select optimal lighting and humidity conditions to grow agricultural products in small home containers.

Infarm Herbs and greens of 200 crops are grown using AI. Data on the state of the crop is sent to the cloud system, and company employees can remotely regulate the degree of illumination, humidity and readiness of the crop for harvesting. Big Data helps regulate the degree and mode of illumination, temperature, pH, concentration and composition of fertilizer supply. Regulates the microclimate.This approach increases productivity by 3 times.
 * Vertical farms Infarm. Germany.

Microsoft
 * Microsoft has developed artificial intelligence for growing vegetables and is successfully growing cucumbers.

Greenbar
 * IT platform “Virtual Agronomist” - digital management of crop production in hydroponics, aquaponics, bioponics, aeroponics systems using artificial intelligence from the Russian manufacturer GREENBAR. Software for managing and monitoring plant growth using artificial intelligence. It has the functionality of microclimate control, nutrition, sterility control and prevention of plant diseases, supply and adjustment of the chemical composition of the nutrient medium. Assessing the condition of plants using video images, identifying diseases and lack or excess of nutrients, monitoring the concentration of oils and nutrients in plants in closed vertical farms of hydroponics, aquaponics, bioponics, aeroponics and micro-drip method systems. Data node transmitter for controlling crop production processes according to all specified external and internal parameters according to 50 sensor indicators.

Agricultural unmanned transport
 * Manufacturer of agricultural equipment John Deere. in the USA Developed the concept of a tractor with an autopilot. Autonomous driving functionality using SaaS (software as a service) integration for autonomous solutions. The financial model of the subscription service will provide farmers with new opportunities and global savings.

Harvesting Automation of processes in greenhouses using machine vision technologies. Using machine vision, robotic harvesters accurately detect, classify and harvest ripe crops. A robot agronomist finds and harvests ripened vegetables (tomatoes, peppers) in a greenhouse. A three-dimensional stereo camera creates a 3D point cloud of ripened fruits. Based on this data, the program creates a three-dimensional scene for searching. Deep learning technologies are used to classify the exact maturity and health of plants.
 * Agronomist MVTEC

Links

 * Modern technologies in agriculture will increase productivity by 70% in 30 years. Russian farmers have already begun to use new solutions.
 * How a virtual agronomist helps feed the whole world
 * BIG DATA TECHNOLOGY IN AGRICULTURE
 * APPLYING MACHINE VISION IN AGRICULTURE
 * Virtual agronomist and autonomous beds: how Russian farms are being taken over world markets
 * Virtual fields. How digital agricultural support programs are developing
 * Digital twins of fields, virtual weather stations and “obedient” harvesters. How IT technologies help Rusagro agronomists
 * How Chechen city farms are conquering the Middle East market