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    Agriculture and usage of Artificial Intelligence!

    Over the years, technology has redefined farming. Technological advances have helped the agricultural industry in more than one way. In many countries, agriculture is the main occupation worldwide. As per UN projections will increase from 7.5 billion to 9.7 billion in 2050.

     As scientists say, AI is based on the principle that human intelligence can be defined so that a machine can copy it to execute the task. It can carry out both tasks that are simple and complex. The main aim of AI includes perception, reasoning, and learning.

    Worldwide, agriculture is a $5 trillion industry. As per various researches conducted on the Agriculture industry, it is very unpredictable. Farmers can hardly predict when the weather strikes or crops get affected by the disease. When a pandemic hits at a global level all of a sudden then it gets harder to manage various processes because most of them are not digital. According to the survey done by the UN, the global population is increasing, and urbanization is continuing. Consumption habits are changing, and disposable income is rising.

    To meet the increasing demand, farmers are under a lot of pressure. They need a way to increase productivity. According to the UN, food production has to increase by 60% to feed an additional two billion people. Farmers need to minimize the risk or at least make them more manageable. Traditional methods are not enough to handle this considerable demand.

    Implementation of AI on a global scale in the agricultural industry is one of the most promising opportunities. AI can change the way agriculture is seen. UN says the challenge is to increase global food production by 50% by 2050 to feed an additional two billion people.  AI will help farmers to improve efficiencies and improve the quality, quantity, and ensure a faster delivery to market.

    Farmers think of AI as something that can be used only in the digital world. But they are not able to understand that it can help them work the physical land. This is because they are not aware of the practical application of AI. Many works still have to be done to educate the farmers on how to implement AI in farming.

    Challenges faced by farmers on traditional farming –

    • The farmers have to prepare the soil for sowing the seeds. This process involves a lot of hard work, and farmers face a lot of challenges in this.
    • Adding fertilizers and chemicals is also a big task for farmers.
    • Sowing seeds is another task that takes lots of time in traditional farming. The distance between the two seeds must be accurate, and the depth of planting must be correct.
    • Irrigation helps to keep the soil moist and maintain humidity. Watering plants in traditional farming can be a very time taking task.
    • Weeds are an unwanted plant that grows near the crop, and removing weeds is a big challenge for the farmers. If not controlled can increase the production cost. It can also absorb nutrients from the soil, which can cause nutrition deficiency in the soil.
    • In harvesting, the ripe crops are gathered from the field. In traditional farming, harvesting is done by the farmers themselves, which takes lots of time.
    • Packaging crops also requires a lot of time.
    • Farmers face difficulty in preparing the soil, sow seeds, and harvest because of frequent climate change.

    Usage of AI in agriculture –

    • Agriculture involves several processes and stages, but all are done manually. AI can help in the most complex and routine tasks.
    • It can simplify crop selection and help farmers to find out what will be more profitable.
    • To minimize the risk of crop failures farmers can use predictive and forecasting analysis.
    • It can help produce crops less prone to disease and better adapted to weather conditions by collecting data on plant growth.
    • It can help in soil insight, recommend fertilizer, and help in weather monitoring. A German-based tech start-up, PEAT, has developed an AI-based application that can tell the nutritional deficiency in the soil called Plantix. Another machine learning-based company called Trace Genomics helps farmers to do soil analysis. Such type of app allows farmers to monitor the soil.
    • It can monitor plants, predict disease identify and remove weeds, and recommend effective treatment of pests.
    • It helps in reduced usage of herbicides and cost savings.
    • It can help in automating harvesting and can also help to improve the overall harvest quality and accuracy.
    • Robotic machines are capable of bulk harvesting with more accuracy and speed. It helps improve the yield size and reduces waste from crops being left in the field. Examples of such machines would be a vacuum apparatus that is used to harvest mature apples from trees and an autonomous strawberry-picking machine1. AI models and machine visions are used by these machines to help the farmers to find out the harvestable produce and help in picking the right crop.
    • More data can be collected and processed significantly with the help of AI by the farmers.
    • It can be used to analyzing market demand, forecasting prices, determining the correct time for sowing and harvesting, proper guidance about water management, crop rotation, the type of crop to be grown, and optimum planting.
    • Farmers can grow more crops with fewer resources with AI.
    • It can provide farmers with real-time insight into their field.
    • It can help farmers to identify which area needs irrigation, fertilization or pest control.
    • With the help of automation, the farmers can solve the problem of labor shortage.
    • It can be used for the intelligent spraying of chemicals. 80% of the volume of the chemicals typically sprayed on crops AI can eliminate. The expenditure on herbicide reduces by 90%.
    • AI sprayers can drastically reduce the number of chemicals used in the fields. Agricultural produce quality will improve and will be more cost-effective.
    • Predicting the price of a crop is a challenging task. Price keeps on fluctuating. But with AI and machine learning can detect disease and pests, estimate the output and yield, and forecast prices. It can guide the farmer on the future price patterns and demand level.

    Difficulties faced in implementing AI –

    • Usage of AI could be complex for farmers as they lack knowledge about its implementation. Farmers would need help in adopting it. AI technology companies along with selling the product will have to provide proper guidance on its usage.
    • Privacy and security threats may cause farmers severe problems. There are no clear policies and regulations around the use of AI in general.
    • Precision agriculture and smart farming raise various legal issues.

     

     

     

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