AI is everywhere, in everything possible… It is at times a Pandora’s box and sometimes a golden egg-laying hen! Using artificial intelligence in agriculture is increasing at a rapid rate, and ensuring both a rise in yields and better use of resources. Ethical and Secure AI Applications in Agricultural Research explains that the field is not just about larger gains; it’s also introducing new ethical and security problems that future professionals will need to face.
One of the reasons AI is important for agriculture is that it helps farmers meet the difficulties created by sudden weather, higher prices of items like fuel and fertilizer, labour shortages and the pressure to increase the amount of food produced from less land or using fewer supplies. AI-driven tools are keeping an eye on crop condition, estimating harvest, clearing irrigation and tracking down plant diseases fast. Recent studies suggest that the AI-in-agriculture market will experience more than 22% annual growth, and applications include precision farming and enhancing the supply chain. With generative AI, people are now getting information and ideas from a virtual expert in agriculture that would have been out of reach just a few years before.
Yet, there’s more to consider here, which can also get challenging. The research paper “Ethical and Secure AI Applications in Agricultural Research: Challenges and Opportunities”, points out that although AI has many advantages in helping agriculture, it can also result in ethical and security issues. There is a lot of talk about how well data is protected. Algorithms benefit greatly from analysing large sets of soil health, weather, crop photos and financial information about farmers. Without proper data protection, there’s a chance that confidential information could be exploited or lost to unauthorised people. It worries me most in areas where technology knowledge is limited and the laws are not fully developed.
Adaptability is a challenge that arises with using AI in agricultural research. Because soil types, weather, crops and how people farm change from place to place, agriculture also changes worldwide. Training these models usually involves data from big, wealthy farms, so they tend to fail those raising crops or animals in more remote areas. As a result, “algorithmic bias” may occur, where suggestions are not suited to the local area and could create more of a divide between big and small farmers.
Security is also a major issue. When more farm equipment and research tools are placed online, cyberattacks become a bigger risk. What if someone hacks an irrigation system or pest control model? Such an attack could cost money and also endanger the world’s supply of food. Researchers say that trust and safety can only be ensured if organisations use solid cybersecurity measures and AI that can be understood and perform routine audits.
Now, when it comes to these issues, things differ in India, but they are also present. For example, India has lots of agricultural AI and companies in the sector make it easier for farmers to manage their farming, right through to selling. With the help of generative AI, Cropin Sage links satellite, sensor and field information to provide farmers with useful insights right away. Today, they are changing the agriculture sector by promoting success, cutting costs and supporting adaptation to shifts in climate.
But there are distinct difficulties present in India. A major obstacle is that reliable internet is still lacking for smallholder farmers in most rural and remote locations. The absence of this infrastructure makes it harder for everyone to profit from AI. Some people struggle with digital literacy; many young users of the digital economy embrace AI quickly, in contrast to those who are older.
Still, there are plenty of big chances available. Precision agriculture is becoming a reality because of AI, letting farmers waste fewer resources and use farming methods that are more environmentally sound. For instance, such systems can tell farmers the best times to water, fertilize and spray for pests, so less water and fewer chemicals are used. In some trials, farmers in India who use AI for predicting pests saw their crop yields improve by 20% and used less pesticide, reducing use by almost 30%.
At this stage, it’s particularly appealing for students and young researchers to get involved in agriculture. Farming’s future is not primarily about growing crops; it is also about learning about data, ethics and technology. People who understand both traditional farming ideas and digital technologies will be sought after. Thinking about the right and wrong of AI is important, and this includes asking who is responsible for the data. What processes are involved when making decisions? Are all the benefits available to everyone or just some people?
Moving ahead, making AI in agriculture truly safe and ethical will call for more than progress in technology. Developing strong data privacy laws, boosting internet access in rural parts and focusing on AI that suits local situations will be required. Farmers will benefit most if governments, tech providers and universities team up to bring AI to everyone.
Overall, the change AI will bring to agriculture relies as much on working with people as it does on developing new gadgets. Right now, the food, agriculture and technology fields are open to new ideas, so it is the ideal time for students and young professionals to join in, speak out, and influence progress. Take AIACAT and start your career in agriculture. Contact us for a free consultation at https://aiacat.com/ or 08071296500