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AI in Agriculture

Agriculture may be the most ancient of occupations, yet its relevance has only become bigger with the imminent threat of food insecurity. According to the UN Food and Agricultural Organization (FAO), the global population is set to reach 9.2 billion by the year 2050.

What is needed, therefore, is greater efficiency within current farming methods as farmers will be required to ‘do more with less. The agriculture industry is now turning to AI for solving the double trouble of the food crisis and food wastage in the wake of locust swarms, climate change, droughts and floods.

How Ai is shaping the future of agriculture?

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  1. AI in crop sowing is used essentially to drive predictive analytics to determine when and how to sow. It helps in making predictions on the right time to plant, apply fertilizers, and harvest based on climate data, historical conditions and market conditions for inputs and outputs. Crops can also be sowed using AI-aided machinery at equidistant intervals and at optimal depths. Microsoft in collaboration with ICRISAT (International Crops Research Institute for the Semi-Arid Tropics), developed an AI Sowing App that uses machine learning and business intelligence from the Microsoft Cortana Intelligence Suite. The pilot had a sample base of 175 farmers who were alerted on their mobile phones about suitable cropping dates, land preparation, and soil test-based fertilizer utilization.
  2. Drone-based images can help in in-depth field analysis, high-efficiency crop monitoring, scanning of fields, long-distance crop spraying, and so on. They can be combined with computer vision technology and IOT to ensure rapid actions by farmers. These feeds can generate real-time weather alerts for farmers.
  3. The concept of sustainable farming requires smart usage of freshwater. We can use AI to simply monitor water usage productivity or automation of irrigation workflows or even analyze historical irrigation data, map it against crop health and yield stats, and find the best water consumption patterns that fulfill all required conditions. Fasal is an Agritech startup that uses weather prediction capability and helps farms alter their irrigation plans making use of free rainwater.
  4. This helps in soil characterization based on the soil below the surface in a particular place. This also helps in understanding the reaction of specific seeds to different soils, the impact of weather changes on the soil, and the probability of the spread of diseases and pests
  5. Images of various crops are captured using Computer Vision Technology under white/UV-A light. Farmers can then arrange the products into separate stacks before sending it to the market. Pre-processing of images ensures the leaf images are segmented into areas for further diagnosis. Such a technique would identify pests more distinctly.
  6. The purchase of insecticides and pesticides contributes approximately 5% to the total cost inputs in agriculture. AI helps to optimize weed and pest management by reducing up to 80% of weedicides and pesticides used currently.
  7. AI-enabled robots for harvesting can lead to huge cost savings by reducing the need for approximately 4 agricultural laborers per acre of land. Furthermore, crops can be sorted according to pre-identified grades at the time of harvest, saving time and enhancing the quality of crops

Benefits of AI in agriculture

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Before we start, let’s make it clear that artificial intelligence is not an annoying know-it-all tech. It only provides businesses with insights that stem from the data they already have — or automates processes that previously required human involvement.

  • Yield improvement. By analyzing operational data and highlighting process inefficiencies, artificial intelligence finds ways for agribusinesses to increase yields without using any extra resources.
  • Profit increase and Cost reduction. AI solutions spot wasteful resource consumption patterns, suggest optimization scenarios and maximize yields without requiring any additional resources and reduce costs dramatically and thereby increasing the overall profits by manifold.
  • Analyzing market demand — AI can simplify crop selection and help farmers identify what products will be most profitable.
  • Alignment with sustainable farming practices. The concept of sustainable agriculture revolves around finding a way to meet current food needs without using up too many resources and leaving the next generations with nothing. AI helps farmers find sustainable patterns of resource consumption to avoid water scarcity and land degradation
  • Predictive maintenance. To reduce downtimes in case of farming equipment failures, agribusinesses can use AI to monitor equipment performance and notify maintenance teams about upcoming failures.

Challenges Ahead

  • Lengthy technology adoption process — Farmers need to understand that AI is only an advanced part of simpler technologies for processing, gathering, and monitoring field data. AI requires a proper technology infrastructure for it to work. So software companies should approach farmers gradually, giving them simpler technology first, such as an agriculture trading platform. Once farmers get used to a less complicated solution, step it up and offer something else, including AI features.
  • Lack of experience with emerging technologies — The agricultural sector in developing countries is different from the agricultural sector in Western Europe and the US. Therefore, tech companies hoping to do business in regions with emerging agricultural economies in addition to providing their products, they’ll have to provide training and ongoing support for farmers and agribusiness owners who are ready to take on innovative solutions.
  • Privacy and security issues — Since there are no clear policies and regulations around the use of AI not just in agriculture but in general, precision agriculture and smart farming raise various legal issues that often remain unanswered. Privacy and security threats like cyberattacks and data leaks may cause farmers serious problems. Unfortunately, many farms are vulnerable to these threats.

Endnote

Unquestionably, the agricultural sector needs a greater impetus from policymakers in addressing the aforementioned challenges. And as you saw AI is a possible way forward in increasing the economic and environmental sustainability of agriculture. Certainly, AI is not without its own shortcomings, but then everything has its advantages and disadvantages.

Thanks to our augmented capabilities, for changing our world dramatically. We’re going to have a world with more variety, more dynamism, more adaptability and of course, more beauty. It is not going to be anything like what we’ve ever seen before. Why? Because what will be shaping those things is this new partnership between technology, nature and humanity. That, to me, is a future well worth looking forward to.

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