Agriculture is one of the most important sectors in our lives. Food is an essential need for every human being. So, it is essential to improve the productivity of agricultural activities with rapidly advancing technology.
One of the most major developments in recent years is Artificial intelligence. We have already begun to implement AI to agricultural activities. one of the most important subfields of artificial intelligence is Machine learning. Basic meaning of machine learning is enabling computers the ability to analyse data and make decisions by their own mimicking a natural human brain sequence to solve problems.
Machine learning is a major part in many fields now including commercial, medical, construction, engineering, and military. One of the major advantages of ML is the faster computations and ability to handle complex tasks with much more accuracy.
So, let’s discuss about how ML helping to shape the agricultural sector.
Maths and computations is some thing most of us don’t like that much. Also, there is a limit for the accuracy of a human brain. But computers are well known for their accuracy in analysing and computing output information. So, will be very easy to let a computer handle many of the mathematical information on their own using Machine Learning.
For example using fertilizers and water for plants/crops is major part in farming. But if we can analyse the data such as NPK (Nitrogen, phosphorus, Potassium) Level in the soil, water level in the soil, temperature, plant development stage, soil moisture, and pH level in different areas cross the farming land using machine learning algorithms, Then we can develop the algorithm to get information about how much water or fertilizers we need to spray in the each area. Ultimately farmers can save money and time by using this method.
Just like that we can use the performance of Machine learning algorithm to improve the productivity in farming and agriculture.
Here are some of the use cases for machine learning in agriculture.
- Pest Control :
we can implement machine learning algorithms for pest control. We can use computer vision and image processing by building ML algorithms to analyse plant images to find harmful animals such as insects and create a controlled pesticides spraying into the plants. This can reduce the affects of pesticides in to the environment.
2. plant disease detection
Plants can affects with diseases at any given time. the major problem is that most of the time when humans can visually see the symptoms, it is tool late. But with Machine learning algorithms, we can analyse the visual data that naked eye can not identify, pretty easily and inform them to a farmers so they can take necessary actions to protect plantation. Some times algorithm can also suggest what actions should farmers take to protect plants.
Following is a Machine Learning algorithm I did develop to detect diseases by enhancing the colour codes.
3. crop growth analyse
With ML, we can analyse and predict when should the crops be harvested and what growth stage they currently in. This can help farmers to manage time productively during harvesting.
4. live stock management:
Technologies such as RFID(radio frequency identification) can be combined with machine learning algorithms can improve the methods of keep in track with farm animals. We can identify and manage a data base about animals and use machine learning can make predictions based on the data such as diseases, pregnancy, age and being too old or specific tasks. Also, we can use machine learning algorithm to monitor and protect animals daily.
4. demand for increasing labour force
there is a constant increasing demand for labour forces but human labour force is declining slowly. So, it important to apply advanced robotic and autonomous systems to perform certain labour tasks. One of the major key areas for developing such systems are advanced machine learning algorithms.
there are other major use cases such as cost and profit analyse, market predictions for agricultural products, weather predictions, real time security monitoring etc.
just like that there are several usages of ML in agriculture that can create a constant demand. In the near future, AI and Machine Learning will play the major part in farmers lives and human already have started to achieve that goal.