Artificial Intelligence, or AI, is a technology that allows computer programs to spot trends in data and adapt their programming accordingly. In ways, AI is almost a simulation of human intelligence; it can improvise, change, and overcome many situations that conventional software fails to perform in. AI can adapt, and in a sense, “program itself” to serve its users better. Meanwhile, traditional programs are bound by the code that its developers write for it and are incredibly inflexible in adapting to new situations. As such, AI is a potent tool, embraced by thousands of companies in the banking sector, entertainment, business, stock trading, etc. In this article, we dive over the scope of AI in Agriculture.
Table of contents
- AI is everywhere:
- How adopting AI in Agriculture can revolutionize how we approach everything:
- AI in Agriculture: Necessary Steps and Policies for Implementation:
AI is everywhere:
In modern-day society, AI is not bound to a physics laboratory or research facility. AI is everywhere. AI is the same technology that suggests tags for you when you upload a new photo to Facebook and separates your spam emails from useful emails. AI detects fraud-attempts in ATM transactions and suggests new videos on Youtube that you are more likely to watch. TikTok, one of the hottest apps of 2020, has its software model entirely based on AI.
Nepal, being an agriculture dependent country, must embrace new AI technologies that help make the agricultural process a lot more effective. AI is not just a shiny new technology, it is a practical tool – and a very powerful one at that when used correctly.
How adopting AI in Agriculture can revolutionize how we approach everything:
AI can be a master-tool to modernize and simplify agricultural processes. All around the world, Artificial Intelligence is revolutionizing agriculture. With climate change, the impotence of soil, and various other factors increasing in effect, multiple companies across the globe have resorted to more innovative measures to improve their agricultural outputs significantly.
1. Analyzing Agricultural Data:
Collecting data effectively and predicting future trends is a big part of agriculture. With the help of AI, farmers can predict future weather conditions, water levels, and even soil capacity. By analyzing what resulted in a large yield of crops and putting it in terms of other factors like season, rain levels, humidity, etc., and tallying it with a prediction of profit margins, farmers can create better plans for their harvest cycles. Cognilytica reports over 75 Million Agricultural smart devices already in use worldwide this year for these particular use cases.
Not only will AI software help increase profit for farmers, but the data collected this way can give budding AI researchers a lot to play around with as well. More data means more information that algorithms have to learn from. More learning datasets means better algorithms – ones that can ultimately predict more accurate results. It’s a positive-sum game in which everybody wins. AI in agriculture is a means that can revolutionize how we approach farming.
2. Crop and Soil Monitoring:
AI can and has been used time and again for better crop and soil monitoring, both of which are incredibly integral to agriculture. A breakout of plant disease or an increase in the population of a particular pest(insect or fungi) can destroy an entire season’s hard work. Pollution can destroy the potency of soil. As such, monitoring soil and crops for any adverse symptoms become incredibly vital.
There are more than enough stories from our Gau-ghars(villages) about people ignoring early signs of disaster, only to end up with barren land or devastated crops. A small effort at first signs of symptoms can easily prevent such a tragedy. A study published on ResearchGate highlights how the majority of farmers are unaware of pesticide types, level of poisoning, safety precautions, and potential hazards on health and environment, a mistake that can easily lead to big consequences.
Early Detection of Plant diseases:
Plant disease detection was one of the first major breakthroughs in Agricultural engineering involving AI. The body of research backing up this technology is growing daily, as evidenced by the growing number of research articles like these ones on international science journals Frontiers in Plant Science, Springer, BioMedCentral, etc.
In the context of our own country, the app Agrovet, which used AI to detect plant diseases from a photo of a leaf, won the 6th annual Microsoft Imagine Cup Global Finals held in 2018. This app, developed by Team SochWare surpassed 49 competitor teams from all across the globe to secure the award. This is a great example of how the right vision can help us take steps forward.
Even now, students in various universities and research centers are using Deep Learning in creative ways to improve the scope of this technology further or make it more accessible or user-friendly to farmers. Many of them lie below the poverty line and are illiterate. AI in the agriculture scene must be easily approachable, and the software easily educatable to the farmers.
3. Use of Robots in crop plantation:
We severely lack behind in using robotic devices in crop plantation. Drones are regularly used in countries all across the globe to distribute crop seeds or spray insecticides in large areas. Most of the farmers and farms, though, have failed to improvise and do it mostly by hand.
The consequence of increasing potency of Pesticides:
While a lack of investment and various other factors affect this inability by farmers to utilize new technologies, this doesn’t mean that they are spared from the consequences. The spraying of pesticides by hand wearing nothing but flimsy protection masks and gloves has caused a significant negative health impact in the farmer’s lives. Not only are the farmers suffering, but their children and further generations are bound to be affected as well. Stories of children being born with congenital disabilities are becoming more and more commonplace. Lung and skin related diseases appear to be on the rise as well, as reported by this study on NCBI.
4. Smarter GMOs(Genetically Modified Organisms):
GMOs have saved millions of people from dying of poverty. GMOs or Genetically Modified Organisms(crops in this context) are genetically modified crop seeds that have been designed to produce better results for farmers. Right now, AI is helping agricultural industries develop better quality GMOs.
While GMO/Genetic engineering might still be a far-off idea for a developing country like Nepal with sub-par research facilities, it can even help researchers by providing necessary data to better engineer GMOs better suited to our local climates. “In order to make sound conclusions about different types of genetically engineered crops and to plan for the future, we’ll need to have sound data about any possible environmental effects of said crops.”, says Anastasia Bodnar, author and Policy Director at Biofortified.
AI in Agriculture: Necessary Steps and Policies for Implementation:
The following steps and policies should be seriously considered to make AI a technology that enhances our agriculture, and to make the change one that sticks on.
- More awareness needs to be raised among private and small-business agriculture business holders about the potential of AI as a potent tool for farming. AI is a tool, nothing more and nothing less, but a compelling one that might be a bit tricky to set up initially but will pay back multiple folds over time.
- People researching AI and its relation with Plant Sciences need to reach out to open-minded agricultural business owners about their study and advancements.
- The government should mandate laws regarding the use of AI in data collection, especially against the misuse of such technologies and regarding the necessary quality standards that all service providers should follow. This will comfort potential users and provide a sense of security.
- More investment in the technology sector by concerned government bodies, especially concerning fields like agriculture, where we already have a solid foothold.
What do you think about the future scope of AI in agriculture? Will we wake up to a future where technology has improved our agricultural output dramatically, or will it struggle to find a place in our society? What are your views? Leave them in the comments section below!