Innovating in Smart agriculture can be difficult, given the farming industry really thrives on generational transfer of family businesses. Currently, 98 percent of America’s farms are family-owned and when these farmers do embrace new technology, it is only once they are convinced it is safe for them, their families and their land and that it will help them to farm better. However, modern agriculture is often pilloried as a problem, and shamed as industrialized or uncontrolled factory farming driven by multinational technology corporations.
On top of agriculture being a digitally gated industry, creating successful technological innovations requires up-to-date detailed research associated with the given sector. Sadly, less than one percent of US citizens have direct experience on farms, and it’s even exceedingly rare for most people in tech to have the level of access necessary to drive agricultural innovation.
This not only makes it extremely hard for tech specialists to garner the research necessary to innovate, but also makes it difficult for farmers themselves to make smart, informed investments in order to enhance food sustainability and production.
In order to bridge this information barrier between farmers and tech specialists, the key is to foster a better understanding of agricultural research, discovery and application. This would have to be an investment that takes a long time to deliver returns, both in terms of skill/knowledge gains about farm operations, and in terms of farm operational productivity and profitability.
On the side of the farmers, this investment includes easing fears about upsetting the status quo, safety issues, monopolization, which can slow or bar much-needed technology. For instance, past and more recent history is littered with examples of where innovative research – artificial insemination, mechanization, transgenic crops – struggled for acceptance once the outcomes were commercialized. Dealing with such fears will require a shift in societal thinking to minimize the oft-held perception that innovation equals risk.
As for the side of the tech specialists, taming the average farmer’s fear of innovation will require working harder to understand the plight of the common farmer, and how to alleviate these issues via technology. The most notable, and accessible solution for this nowadays is simply for these tech companies to engage more will farmers, and the agricultural industry as a whole. While there are some issues here around farmer bandwidth, technologist access and context, and cultural differences, it’s without a doubt that the entire agricultural sector would be well served by an “in-between” set of resources for technologists who are thinking about building for agriculture.
Luckily, while the enhanced unison between farmers and tech specialists may take some time, there are already numerous, flexible technologies available that, while they aren’t specialized for agriculture, can still offer quite the positive impact. Among the variety of devices and applications available of the choosing, the most prominent in terms of impact is surely, by far, artificial intelligence (AI)
AI in agriculture, also known as precision agriculture, is the application of artificial intelligence (AI) solutions in the agricultural industry. As boosting food production and security becomes a priority around the world, AI in agriculture is taking off fast. The overall AI in the agriculture market came in at USD 1 billion in 2020, but is expected to grow at a CAGR of 25.5 percent, bringing the predicted market value up to USD 4 billion by 2026.
The rapid growth is unsurprising to say the least, as AI holds a tremendous amount of potential in terms of revolutionizing the daily processes of farmers.
For example, organizations are already taking advantage of computer vision & deep-learning algorithms to process the information or data received by drones to keep an eye on crop & soil health. AI drones in agriculture can be used remotely and continuously monitor the quality of soil for ensuring the healthy growth of the crops. By providing a detailed summary of field soil contents, ML and AI applications in agriculture will give insights into the quality of the soil and help farmers in taking immediate decisions towards improving the quality.
Furthermore, AI can help farmers to find various competent approaches to safeguard their farms from pests or weeds. Currently, weed control became the primary concern for the farmers, and that is an ongoing defy as herbicide conflict becomes a routine place. According to research, the effect of uncontrolled weeds on soybean crops and corn alone made losses of around $43 billion for farmers. AI can monitor weeds growth remotely, control pests, and yield healthier crops, allowing farmers to get rid of 80 percent of the chemicals that are usually sprayed on crops.
Finally, AI and automation can help address the current workforce shortage. Agricultural work is hard, and much like in other sectors, labor shortages in this industry are nothing new. However, driverless tractors, smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting are some examples of how farmers can get the work done without having to hire more people. Compared with any human farm worker, AI-driven tools are faster, harder, and more accurate.
Overall, while AI, ML and other automated applications can offer a wealth of advantages to farmers, in order to achieve the desired levels of food sustainability, further innovation is still very much necessary. For now, it is critical for technology providers to think about a few things, such as how to improve their tools, how to help farmers address their challenges, and how to easily and understandably convey that machine learning helps solve real struggles, such as reducing manual work. By doing so, tech specialists can help farmers be able to build a robust technology ecosystem that will stand the test of time, and offer a global food production and security like never before.
