Artificial Intelligence In “Agriculture”, 

Is It Ehical?

Anil Kumar Kummari

Today, the agricultural sector has grown into a highly competitive and globalised industry, where farmers and other actors have to consider local climatic and geographic aspects as well as global ecological and political factors in order to guarantee economical survival and sustainable production. Feeding a growing world population asks for continuous increases in food production, but arable land remains a limited resource. New requests for bio energy or changing diet preferences put additional strains on agricultural production, while settlement and transport consume increasing shares of land. Expected and observable changes in global climate, shifting rainfall patterns, global warming, droughts, or the increasing frequency and duration of extreme weather events endanger traditional production areas and bring new risks and uncertainties for global harvest yields. To cope with these challenges, Agriculture requires a continuous and sustainable increase in productivity and efficiency on all levels of agricultural production, while resources like water, energy, fertilizers etc. need to be used carefully and efficiently in order to protect and sustain the environment and the soil quality of the arable land. The complexity of the challenges are increased by other short-term events, which are difficult to predict, such as epidemics, financial crisis, or price volatility for agricultural raw materials and products.

Agriculture is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There is unpredictable weather, changes in soil quality, and the ever-present possibility that pests and disease may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain. 

So, what is slowing the progress of drones in agriculture? Beyond the barriers to widespread drone adoption in all industries—safety of drone operations, privacy issues, and insurance-coverage questions—the biggest agricultural concern is the type and quality of data that can be captured. To address this, the industry will push for more sophisticated sensors and cameras, as well as look to develop drones that require minimal training and are highly automated. There are incredible opportunities for AI in agriculture.  The problems are that most folks do not understand how to deploy AI in meaningful or cost-effective ways in agriculture.  I have found that there is a vast knowledge void between farmers and technologists that means that technology is poorly applied on one side, or farmers resist adoption on the other side because they do not understand the how it is meaning full. The rising demand for agriculture robots on a global scale is attributed to the staggering rate of growth in the world population and a relative reduction in the average available agriculture workforce. This is a direct consequence of an increase in urban migration. Agriculture robots are expected to replace human labour and can thus help overcome the scarcity of physical labour.

The trouble with farming at present is that it is still something of a gamble, with a number of troublesome variables such as the weather, commodity prices, and fuel prices in play. However, AI cannot give the accurate comparison with the humans. It eliminates the workforce in Agriculture, which is highly dependent sector. Until GM crops are not be adopted widely because of against nature. We have to point out that AI in agriculture is also against nature. For the developed countries, it is up to an Ethical. Nevertheless, for developing and under developing countries it is going to be challenging factor as unethical.

References:-

ü https://agfundernews.com/the-challenges-for-artificial-intelligence-in-agriculture.html

ü https://link.springer.com/article/10.1007/s13218-013-0275-y