Application of ML in Agriculture

  INTRODUCTION

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

pre-harvesting machine learning is used to capture the parameters of soil, seeds quality, fertilizer application, pruning, genetic and environmental conditions and irrigation. Focusing on each component it is important to minimize the overall losses in production. 

 How Is Machine Learning Used in Agriculture?

Machine learning (ML) has already begun to play an important role in making agriculture more efficient and effective. Precision ag relies on the gathering, processing, and analysis of data for more efficient agricultural production. On the modern farm, you can collect data with the use of advanced technology, such as:

• autonomous vehicles, 
• variable rate technology, 
• GPS-based soil sampling, 
• automated hardware, 
• telematics, 
• software, 
• sensors, 
• cameras, 
• robots, 
• drones, 
• GPS guidance, and 
• control systems.

There is no doubt that equipment based on ML has brought the farming system to a new level of efficiency. This technology has increased crop productivity and enhanced tracking, harvesting, processing, and marketing in real-time.

This technology aims to provide and act on precision ag insights of increasing quantity and quality. However, where does ML fit in, and how is it used in agriculture? To answer that question, first, you should be clear about machine learning.

How does machine learning make farming more efficient?

Precision agriculture is the goal of every farmer. The combination of emerging technologies to achieve this are on our doorstep, including ML and Internet of Things (IoT) hardware.

Today’s farmers can access so much data—more data than they can handle without the assistance of machine learning technology. ML can analyze tons of data quickly. Based on that information, it can suggest a certain strategy. For example, it could suggest the best time to plant to avoid pests and disease.

More examples of the use of machine learning in agriculture :

The following are just a few examples of how the agricultural industry is using and can use ML:

 Robots – Hyper-efficient AI harvesting bots can replace human workers in the agricultural sector and reduce labor costs. They can also help farmers to protect their crops by keeping track of and spraying weeds.

Watering – Farmers use AI to monitor growing areas for crop humidity, soil composition, and temperature. This AI use results in increased yields due to the optimization of water and fertilizer use.

Resource management – AI can help farmers save energy, reduce pesticides, and shorten the time to market.

Optimization of nitrogen in the soil – Nitrogen is a vital nutrient that allows plant growth. Although nitrogen is prevalent in the ground and the atmosphere, plants can only use a very small percentage of the nitrogen in the soil. Farmers can keep these inorganic nitrogen levels at optimum levels with the help of machine learning technology.

Species breeding – Species selection is a painstaking task involving searching for specific genes that will ensure effective responsiveness to water and nutrients. Ideal plant species will cope with climate change, be resistant to disease, have higher nutritional content, and taste better.

Machine learning allows us to draw from decades of field data for detailed crop performance analysis. A probability model from this data predicts which genes will contribute a sought-after genetic advantage to a plant.

Species recognition – Traditionally, plant classification has been done by basic comparisons such as the color and the shape of the leaves. Machine learning enables much more complex, accurate, and faster analysis of plants using more sophisticated techniques such as analyzing leaf vein morphology.

Although ML-driven farms are in their infancy, these few examples indicate that they are already evolving into factories run by machine learning. At the current time, machine learning solutions in agriculture tend to deal with individual problems. 

        How Is Machine Learning Used in Agriculture?

Machine learning (ML) has already begun to play an important role in making agriculture more efficient and effective. Precision ag relies on the gathering, processing, and analysis of data for more efficient agricultural production. On the modern farm, you can collect data with the use of advanced technology, such as:

• autonomous vehicles, 
• variable rate technology, 
• GPS-based soil sampling, 
• automated hardware, 
• telematics, 
• software, 
• sensors, 
• cameras, 
• robots, 
• drones, 
• GPS guidance, and 
• control systems.

There is no doubt that equipment based on ML has brought the farming system to a new level of efficiency. This technology has increased crop productivity and enhanced tracking, harvesting, processing, and marketing in real-time.

This technology aims to provide and act on precision ag insights of increasing quantity and quality. However, where does ML fit in, and how is it used in agriculture? To answer that question, first, you should be clear about machine learning.

How does machine learning make farming more efficient?

Precision agriculture is the goal of every farmer. The combination of emerging technologies to achieve this are on our doorstep, including ML and Internet of Things (IoT) hardware.

Today’s farmers can access so much data—more data than they can handle without the assistance of machine learning technology. ML can analyze tons of data quickly. Based on that information, it can suggest a certain strategy. For example, it could suggest the best time to plant to avoid pests and disease.

More examples of the use of machine learning in agriculture :

The following are just a few examples of how the agricultural industry is using and can use ML:

 Robots – Hyper-efficient AI harvesting bots can replace human workers in the agricultural sector and reduce labor costs. They can also help farmers to protect their crops by keeping track of and spraying weeds.

Watering – Farmers use AI to monitor growing areas for crop humidity, soil composition, and temperature. This AI use results in increased yields due to the optimization of water and fertilizer use.

Resource management – AI can help farmers save energy, reduce pesticides, and shorten the time to market.

Optimization of nitrogen in the soil – Nitrogen is a vital nutrient that allows plant growth. Although nitrogen is prevalent in the ground and the atmosphere, plants can only use a very small percentage of the nitrogen in the soil. Farmers can keep these inorganic nitrogen levels at optimum levels with the help of machine learning technology.

Species breeding – Species selection is a painstaking task involving searching for specific genes that will ensure effective responsiveness to water and nutrients. Ideal plant species will cope with climate change, be resistant to disease, have higher nutritional content, and taste better.

Machine learning allows us to draw from decades of field data for detailed crop performance analysis. A probability model from this data predicts which genes will contribute a sought-after genetic advantage to a plant.

Species recognition – Traditionally, plant classification has been done by basic comparisons such as the color and the shape of the leaves. Machine learning enables much more complex, accurate, and faster analysis of plants using more sophisticated techniques such as analyzing leaf vein morphology.

Although ML-driven farms are in their infancy, these few examples indicate that they are already evolving into factories run by machine learning. At the current time, machine learning solutions in agriculture tend to deal with individual problems.