What is Machine Learning?
Machine Learning is the process of using artificial intelligence and computer algorithms to learn systematically. These algorithms can learn from the data and improve over time as more data is ingested. Machine learning has become more popular in recent times and this can be attributed to better computing technologies and the evolving big data industry. There are three different types of Machine learning.
- Supervised Learning
This is when the model makes use of mapped or labelled datasets to adjust the weights of the trained model. These weights are adjusted by going over the given data set and the best weight is reached after a number of iterations are run. These iterations are called epochs. This machine learning algorithm uses cross validation for its training process. The goal here is to select the best weights for the model so that when a new dataset is fed into the model, prediction or classification can be carried out accurately.
- Unsupervised Learning
With this type of learning, the model is made to learn on its own without any labelled data. Instead the model is forced to discover hidden structures and patterns in the dataset. This model is then used to solve clustering or association problems.
- Reinforcement Learning
In reinforcement learning, the model is forced to learn and make decisions in a complex environment. The model is either applauded and receives rewards when the right decision is made or punished when the wrong decision is made. This way, the model is forced to go in the right direction based on the feedback received after every iteration. This trial and error process is what makes up reinforcement learning.
These different methods describe the way the algorithm learns from provided data. Obviously, data is a crucial component of any machine learning algorithm. The quality of data and size of data can go a long way to determine how efficient the algorithm is. As data increases, the algorithm is able to adapt to newer examples and learn important features. These features learned can now be applied to forecast or predict, carry out classifications etc. with minimal errors. This is what makes machine learning very useful as it can be applied to solve various business problems and can help companies make more informed decisions about the future. It can also be used to create smart and automated systems in industries and various sectors one of which is the agricultural sector.
Machine Learning in Agriculture
Farms and the Agricultural sector in general now benefit greatly from Machine Learning algorithms and Artificial Intelligence as they can be applied in various ways to provide useful insight and to create efficient automated systems. The Agricultural sector can be divided into two major areas.
- Arable Farming
- Pastoral Farming
Arable Farming is the growing of crops. These crops have different stages and face different problems from time to time. This means that to achieve maximum quality produce, the crops need to be monitored and studied closely to determine its need at each point in time. In the past, this process was carried out manually but in this new age of Artificial intelligence and machine learning, sensors and drones can be used to collect data periodically from the farm. This data is then used to analyse and monitor the growing process of the crops. Some key properties that can be monitored include the soil quality, water level, presence of weed and so much more. The data accumulated is in turn used to control water valves, release pesticide to certain problem areas, apply fertilisers etc. This in turn helps create a smooth and much needed automatic system on farm lands.
With the advancement in computer vision and image processing, drones can now be used to take pictures which are in turn processed and analysed. The machine learning algorithm is able to interpret these images and this can be used to detect weeds among crops, detect diseased crops, differentiate between different crop species and determine the growth level of crops. All these and more can now be done without much human intervention. The acquired data can be used to increase productivity and farm produce and also help the farmer discover major problem areas and carry out the needed analysis and to make informed and effective business decisions.
Pastoral farming is the rearing of livestock. Just like in Arable farming, Machine learning algorithms also help accurately predict animal weights, egg and milk production etc. This gives the farmer adequate insight to adjust diets and food nutrients for the livestock. In farms today, livestock behavioural pattern can be monitored with the aid of different kinds of sensors and cameras. The movement and feeding patterns of these animals can be interpreted and gathered as useful data to determine the stress level, health and nutritional need of the animals and livestock. Machine Learning is used to bridge the gap between the gathered data and actual useful application. All of this helps to provide useful real time insight to the farmer. These insights can be used to create effective dietary plans. This definitely has been significant in reducing production costs and increasing profit margins in this area of farming.
Organisations currently leveraging Machine learning in Agriculture
- Blue River Technology
This company combines the field of machine learning, robotics and computer vision to detect potential problem areas on the farm and help the farmers take appropriate mitigating action. They mostly make use of automation and robots powered by AI to protect the growing crops from weeds. The company developed a robot called the See & Spray robot which helps with weed control. The machine learning algorithm and computer vision helps detect weeds early in crops and the AI powered robots help with the spraying of herbicides. This method has helped reduce, herbicide resistance drastically while simultaneously saving about 80 percent of funds spent on herbicides. Blue River technology is very successful but occasionally face challenges such as glitches and faults of machinery and robots.
- PEAT
This Berlin based company specialises in using Machine Learning to in identifying soil defects and nutrients. They developed a product called Plantix which uses computer vision combined with machine learning to detect defects in the soil. This product uses Computer vision and images for the detection and to also provide possible solutions and tips to combat the detected problem.
- Trace Genomics
This California based company also focuses on using machine learning to provide soil analysis to farmers. This analysis is carried out with the aim of reducing defective crops, maximising soil potential and improving overall yield.
- Ibex Automation
A UK based company that leverages the power of machine learning to produce high precision robots for farmers and agricultural processes. The company focuses on developing autonomous Machines for weed detection and spraying.
General challenges faced in this field
One of the major problems faced when adapting Machine learning methods to farming is long adaptation periods. The process of gathering data, processing, analysing and providing useful technology to perform various tasks takes time. The whole process needs to be studied and this may lead to lengthy adaptation periods. Different farms and farming processes have different needs unique to them. Therefore proper technology and infrastructure needs to be put in place.
Conclusion
Presently, there are ideas to develop more efficient field robots that would assist humans to carry out farm operations such as weeding, drilling, animal and plant sensing etc. These robots would revolutionise the type of equipment’s and machines currently used on the farms and would create a purely automated environment. Also, the field of Computer vision is gradually advancing and improving in precision. This would help accommodate various types of farm images which would in turn drastically improve Machine learning processes on farms such as weed detection.
It is clear that adapting Machine learning in farms would help reduce cost of production in huge numbers, minimise loss and go a long way to help farmers achieve better results with minimal efforts. In conclusion, machine learning combined with various other technologies has the ability to revolutionise the agricultural sector.