Artificial intelligence for agriculture

AI-in-agriculture
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Artificial intelligence can be applied in many ways in agriculture:

Automated irrigation systems

An automated irrigation system refers to the operation of the system with no or just a minimum of manual intervention beside the surveillance.

Several types of such systems exists

Closed Loop Systems – based on a predefined irrigation scheme the control system takes over and makes detailed decisions on when and how much water to apply

Open Loop Systems – based on the amount of water to be applied and the timing of the irrigation

Volume Based System – The pre-set amount of water can be applied in the field

Time Based System – works with time clock controllers

EFFECT OF USAGE

i)Reducing production costs of vegetables, making the industry more competitive and sustainable.

ii)Maintaining (or increasing) average vegetable yields

iii)Minimizing environmental impacts caused by excess applied water and subsequent agrichemical leaching.

iv)Maintaining a desired soil water range in the root zone that is optimal for plant growth.

v)Low labor input for irrigation process maintenance

vi)Substantial water saving compared to irrigation management based on average historical weather conditions.

POTATO SORTING SYSTEM

A robotic system that sorts and detects diseases in potatoes.

Effect of usage

Reducing production costs of vegetables, making the industry more competitive and sustainable.

Maintaining (or increasing) average vegetable yields

Minimizing environmental impacts caused by excess applied water and subsequent agrichemical leaching.

 Maintaining a desired soil water range in the root zone that is optimal for plant growth.

Low labor input for irrigation process maintenance

Substantial water saving compared to irrigation management based on average historical weather conditions.

Facial recognition of livestock

Facial recognition of cows in dairy units can individually monitor all aspects of behavior in a group, as well as body condition score and feeding.

When it comes to lameness, measuring the arch in the cows back could give an early sign of the problem

EFFECT OF USAGE

With this system it is possible to feed cows a lot less expensively if you know what they will and will not eat.

Possible to fix a lame cow before she shows you the signs of lameness, it can save months of lower production.

“PROSPERO” THE SWARMING FARMBOT

“Prospero” is the working prototype of an Autonomous Micro Planter (AMP) that uses a combination of swarm and game theory and is the first of four steps. It is meant to be deployed as a group or “swarm”. The other three steps involve autonomous robots that tend the crops, harvest them, and finally one robot that can plant, tend, and harvest autonomously transitioning from one phase to another.

Effect of usage

The application of the system increases the productivity of land on a per unit basis.

A swarm of small robots like Prospero would have the ability to farm inch by inch, examining the soil before planting each seed and choosing the best variety for that spot.

This would be maximizing the productivity of each acre, allow less land to be converted to farm land, feed more people, and provide a higher standard of living for those people because they would spend less of their money on food

Autonomous Early Warning System for Oriental fruit fly (Bactrocera dorsalis) outbreaks

This autonomous early warning system, built upon the basis of wireless sensor networks and GSM networks effectively captures long-term and up-to-the-minute natural environmental fluctuations in fruit farms.

In addition, two machine learning techniques, self-organizing maps and support vector machines, are incorporated to perform adaptive learning and automatically issue a warning message to farmers and government officials via GSM networks.

EFFECT OF USAGE

The use of autonomous early warning system for detecting pest resurgence is an essential task to reduce the probabilities of massive Oriental fruit fly outbreaks.

By preventing pest outbreaks, farmers would be able to reduce their dependence on chemical pesticides. Chemical pesticide abuse often brings harmful consequences to human health and natural environments.

 

The proposed early warning system can be easily adopted in different fruit farms without extra efforts from farmers and government officials since it is built based on machine learning techniques, and the warning messages are delivered to their mobile phones as text messages.

 

The ENORASIS Wireless Sensors Network

The ENORASIS project uses a network of sensors in the fields to determine how much water to give their crops through subsurface drip and micro-irrigation systems. The sensors collect environmental and soil conditions such as soil humidity, temperature, sunshine, wind speed, rainfall and the water valves to quantify water already added to the fields.

EFFECT OF USAGE

ENORASIS combines weather forecast and sensor data about the farm’s crops to create a detailed daily irrigation plan that best suits the needs of each crop.

The model also includes crop yield data and energy and water costs, helping farmers decide whether extra irrigation will increase yields profitably or cause a loss.

VEEPRO

This AI expert system is able to prescribe feed rations, medications, health and welfare conditions for livestock

It can recommend the mating partners for improving genetic potential of offspring.

The expert system is able to perform complex analysis of health, reproduction status of individual or groups of animals, to keep track of production and recommend operational measures to be taken in order to improve the farm performance.

EFFECT OF USAGE

Gives information about dairy cattle improvement and health care.

Supports dairy husbandry, dairy cattle improvement and health care.

 The system can develop and implement fine-tuned breeding programs. The most important selection criteria are milk production, fat and protein percentage, age durability, functional traits (udder, feet and legs), fertility, health, calving ease, and type. The indexes for calculating the breeding values are constantly updated according to the newest scientific insights.

Decision Support System (DSS) for Greenhouse Tomato Yield Prediction using AI Techniques

This system involves a set of Artificial Intelligence based techniques:

  • Artificial Neural Networks (ANNs)
  • Genetic Algorithms (GAs)
  • Grey System Theory (GST).

Use of artificial intelligence based methods can offer a promising approach to yield prediction and compared favorably with traditional methods.

EFFECT OF USAGE

  • The system allows to predict the environmental conditions that influence the growth and productivity of tomato plants including air temperature (day and night), fruit temperature, radiation, CO2 concentration, fruit load, plant density, stress etc.
  • DSS makes possible to adjust the fluctuation of temperature affecting mostly the time of fruit ripening and rate of fruit growth.

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