Machine Learning in Agriculture: Advantages and Uses

“Machine intelligence is the last invention that humanity will ever need to make.” 

~Nick Bostrom

 

Machine Learning is a very vast concept and it can be very well applied in the field of modern day agriculture. With the help of machine learning in agriculture we can check the seeds before sowing them and make better and more healthier seeds that will yield better crops. 

 

Agriculture is one of those areas in which machine learning is being used fastest. By using artificial techniques and methods so many issues are being removed related to precision and adequate solutions are being found out for every problem. 

 

For every country agriculture is the backbone of the economy. You can take example of India, Australia and many European and African countries where crops are grown in abundance and exported to other countries as well. Moreover, there are so many types of fruits, spices, vegetables and other agricultural products that one country lacks and another country provides. 

 

So, without agriculture it is next to impossible to boost the global economy and meet the demands of the people at the same time. We know at what speed the population is increasing and how much pressure it has put on farmers and the whole agriculture sector to grow more to satisfy all. 

 

Due to this constant pressure so many issues arise related to productivity, fertility, efficiency of the soil, yield and other factors. 

 

Let us understand the machine learning agriculture system a little better. In machine learning everything is derived through a learning process. 

 

The methods are derived after learning or experiencing a particular task. There is data which acts as an example for the machine learning system and based on that set of data it learns and responds to a particular task. This learning increases and grows day by day and the system becomes more learned and powerful. 

 

Lots of mathematical and statistical techniques and models are used in machine learning in agriculture to measure the performance. Once the algorithms and models are set, they can be used for data testing. 

 

Functions and Examples of Machine Learning in Agriculture

 

There are two basic functions of machine learning in every field including agriculture.

 

1. Supervised Learning

 

When using machine learning in agriculture the input data correspondingly matches with the output. So the main aim of supervised learning is to show the results in such a way that the inputs will match with the outputs. Now what if the input is missing? 

 

In this case the results will show error as the output will also be missing because it won't have a corresponding input to match itself with. So, this method of supervised learning helps in dealing with such disappearances and show the accurate results in case input data is missing.

 

2. Unsupervised Learning

 

In case of unsupervised machine learning in agriculture, the main objective is to find out if there are any hidden patterns. Sometimes unlabeled data is used because of which certain hidden patterns are formed.

 

Examples of Machine Learning in Agriculture

 

There are many countries using machine learning in agriculture. For example in Amsterdam sensors are being used to gather data about fertility cycle, diets and heat stress about the animals and crops. 

 

If we take cows for example then in Amsterdam, there are certain machine learning algorithms that are able to figure out the health of the cow using the data received from white blood cells extracted from its milk.

 

Another example is getting the yield and cropping pattern of the crop on the farmer's computer or his tablet. With this information he can figure out which portion of the land is harvestable and which is non-harvestable. Along with it the weight and height of the crops can also be measured.

 

Also Read | Uses of Machine Learning in Healthcare

 

Advantages of Using Machine Learning in Agriculture

 

“Within a few decades, machine intelligence will surpass human intelligence, leading to The Singularity — technological change so rapid and profound it represents a rupture in the fabric of human history.”

— Ray Kurzweil

There are many benefits of Machine Learning in Agriculture. Given below are some of the most important ones:

 

1. Suitable time for crops

 

To get the most out of the crops it is important to sow and cultivate them at the right time. It is also important to choose the right crop based on the season and soil. Otherwise due to mismatch you will not get the desired results. 

 

With the help of machine learning farmers can easily predict and forecast the right cropping time and the right match between the soil and crop. You can find the lucrative crops that will give you the best returns.

 

2. Crop Yield Patterns

 

It is important to the status of the crops that you have sown. Finding the real time data about the field conditions, crop conditions, height of the crops, diseased crops is necessary. 

 

Not only will it help you compare this information with your past cropping performance but also judge if everything is being done accurately or not. With machine learning you can use drones and many other gadgets that will help you gather this data.

 

3. Water and Irrigation

 

Judging the water requirements for the crop is important. Adding too much water can destroy the crops and adding too little water can dry them up. So, based on the past requirements you need to judge how much water will be needed in this season also. 

 

Machine learning will help you undermine the moisture requirement of the crops. Water management and irrigation are two very essential elements in agriculture.

 

4. Agribots

 

Employment of laborers for fields, during the harvesting time and throughout the whole agricultural process contribute to cost. This cost accumulates and rises when the labor has to sit idle because there is no work. 

 

With the help of machine learning, agribots can be used to estimate the time when you will need labors on the field. 

 

So you know when you have to employ workers. Skilled workforce is needed during the end phase and when you know the time you need them you can save the unnecessary cost of employing them beforehand.

 

5. Farm animals

 

Farm animals need to be kept healthy so that they can be bred and the animal produce is also of superior quality. For female animals pregnancy checks, dietary requirements and milking duration etc can be estimated using machine learning. 

 

Therefore, the biggest advantage of machine learning is keeping the farm animals healthy and productive throughout the cycle.

 

Also Read | Structured Machine Learning

 

Uses of Machine Learning in Agriculture

 

“Machine learning will automate jobs that most people thought could only be done by people.” 

~Dave Waters

 

Given below are some of the uses of Machine Learning in Agriculture:

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Uses of Machine Learning in Agriculture

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1. Analyze Market Demand

 

Machine learning in agriculture can be used to analyze the market demand of the crops. Sometimes farmers are unable to produce the crop which is in high demand because they did not know about it. 

 

Forecasting the demand will help them produce what will sell more and this way they can get better returns for their produce. If the crop lies unsold then farmers are forced to sell it at a minimum support price. 

 

In this case, they have to settle for less money and suffer losses. Machine learning can help prevent all such situations related to market demand.

 

2. Risk Management

 

Risk management is another area in which machine learning can help in agriculture. Risks can arise due to many factors- external related to environment, government rules and weather changes. 

 

Therefore, by using machine learning we can estimate the possibility of risks and mitigate it as quickly as we find it out. It is better to control the risk factors than having the whole crop produce go to waste.

 

3. Breeding Seeds

 

Farmers have to check the quality of seeds before sowing them. Breeding seeds means mixing different types of seeds to produce something unique or a different species of crop. 

 

To decide which seed can be bred with which depends on proper selection. Machine learning can help in determining which seed will mix with and give the best results. 

 

Breeding wrong seeds can lead to disastrous results and the produced crop can even be harmful for consumption. Rightly bred seeds will always help in giving the best outcome. Machine learning can gather the relevant data and predict the results.

 

4. Crop Protection

 

Crops need to be protected from so many things. Bad environment, diseases, fertilizers and even climate can damage the crops. Farmers can take insurance for their crops or they can also resort to machine learning techniques that can help farmers get daily reports about the crop health and what can damage it. 

 

Farmers can get a 360 degree view of the crops. They can find out how much  fertilizer is needed for the crop and the maximum limit above which the crop will be damaged. Machine learning can be a great source to protect the crop from any sort of attack.

 

5. Soil Health Monitoring

 

Soil has to be prepared before crops are sown. Not every kind of soil is suitable for every crop type. Due to soil pollution the soil becomes acidic and it creates fertility issues. 

 

Therefore, finding the perfect soil so that soil is fit for growing crops is important. Machine learning can help us test the soil type and the contents in the soil. 

 

6. Harvesting

 

During the harvesting season there is lots of work that farmers have to do. Right from hiring skilled workforce to cutting the crops and sending them to the next process. 

 

Machine learning can assist the farmers in a variety of ways and reduce their workload. It can gather data from multiple sources and from past crop seasons can prepare a plan of action for the farmers.

 

Also Read | Machine Learning in Daily Life

 

Agricultural productivity can be enhanced using machine learning and along with it any harmful impact of it on the environment can be reduced. By relying on forecasting and data a lot of agricultural problems can be eliminated. There are many platforms on which you can find relevant information related to machine learning and how it can be implemented in agriculture.