Artificial Intelligence in Agriculture
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Agriculture by definition is the practice of cultivating soil, growing crops and raising livestock with the broader goal of sustaining human life and providing economic gain. It is the most important aspect for our survival. Agriculture has always been the foundation of human civilization.
The Neolithic Revolution was an important phase in human advancement. Human beings transitioned from hunter-gatherer nomadic lifestyle to a settled one. This was triggered by the development in agriculture where plants and animals were domesticated. Consequently, permanent societies came up and expanded due to sufficient supply of food. This set up the foundation of urbanization. But today, it has to catch up with current technology trends.
The civilization of human beings has led to many advancements among them technology, which affects every part of our lives today. One area of interest in technology is the Artificial Intelligence (AI) field. AI refers to the ability of a computer to exhibit human mind capabilities in perceiving, learning, making decisions or solving problems.
A subset of AI known as Machine Learning (ML) focuses on models learning by themselves, with more data provided, to perform specific tasks with greater accuracies. ML inspires another technique which mimics how the human brain filters information known as Deep Learning (DL). Based on the human neural system, DL has deep neural networks made up of parameters and layers. The three main neural architectures are Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Recursive Neural Networks (RvNN).
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Agriculture has been at the centre of human industrialization. Its success is very critical for the advancement of a society.
“A subset of AI known as Machine Learning (ML) focuses on models learning by themselves, with more data provided, to perform specific tasks with greater accuracies. ML inspires another technique which mimics how the human brain filters information known as Deep Learning (DL). ”
AI has also played a vital role in revolutionizing agriculture today. Some AI applications may have focused on certain challenges, yet have consequential benefits to agriculture. A good example is weather prediction. Sowing seeds is one of the earliest stages of farming. It is critical to ascertain the climatic conditions such as rain, humidity and temperature in order to avoid the seeds’ failure to germinate. Today, AI can predict weather with a relatively high accuracy, empowering the farmers to be better decision-makers.
The image recognition approach of AI is also used to monitor the state of health of the soil, plants and animals. An easy way to identify nutrients deficient in soil is to study the health of the crops. Smart irrigation on the other hand is more precise with the soil properties such as moisture level, temperature and pH.
Automated sensors would detect any shortage, which would then be aptly attended to immediately. Same with automating key livestock farming processes such as milking. This machine to machine communication eliminates the need for more manpower.
Weeding is a laborious process involving removing unwanted plants. It can be done manually or through the use of herbicides. Common application of AI include using CNNs to differentiate weeds from crops. This results in only targeted weeds being sprayed and not the whole farm. Consequently, less chemicals are applied to the farms, also saving on extra expenditure.
“Technology is currently a train moving at full speed, you have to run to jump on it or be left forever. ” — Titus Malo, IT Consultant
Lastly, yield prediction in crops and animals is the end result of crop genotype, the management practices and environmental factors. Environmental and management data are fed into CNN and RNN in order to try and predict the estimated yield. By analyzing the consumer behaviour of livestock and the plants’ health, a fairly accurate prediction is possible.
In cases where such detailed data is unavailable, remote sensing becomes the choice. Clearly, all these roads eventually lead to the application of deep learning techniques to solve the farming problems.
The application of AI in agriculture is part of a broader field known as Precision Agriculture (PA). PA is defined as making use of farming practices that are accurate, concise and controlled when it comes to crop growing and livestock rearing.
Information technology is a critical component along with GPS guidance, sensors, robotics, automated hardware, control systems, autonomous vehicles and telematics. The primary goal of PA is profitability, efficiency and sustainability attained through the combination of PA technology and equipment.
Why Technology in Agriculture?
By 2050, we will need 70% more food produced. This is because of the global population which today stands at about 7.7 billion. By the end of this decade in 2030, it is expected to reach 8.5 billion, and even get to 9.7 billion by 2050. Guaranteed there exists uncertainty in precise population projection due to factors such as fertility, migration and mortality, the indisputable fact is that there will be a rise in the future. It has already been pointed out, going by the current trend, that we will fall much shorter in terms of producing enough food to sustain the projected 2050 population.
Among the measures proposed to mitigate the challenges include increasing the food production without necessarily expanding the agricultural land. Additionally, the amount of food waste should be reduced along the whole chain from field to the table. Climate change is only worsening the situation.
Conclusion
AI has proven to be a game-changer in whichever field it has been adopted, and Agriculture is no exception. But the challenges standing on the way of its successful implementation are hard to be ignored. Some of them include lack of uniformity in terms of research being done globally. When different researchers use varying measures to come up with conclusions, replicating their works with the intention of advancing becomes difficult. These challenges include data being collected with unknown device specifications, or worst case scenario is not making the same data available. This can be attributed to collection of biological data being not just tiresome, but also expensive.
The next article focuses on Precision Agriculture in terms of its definition and implementation. By discussing the finer details of the present status and how best to move going forward, the role that AI is playing is even greatly appreciated.