Artificial intelligence in agriculture and related fields is experiencing a revival. Electronic mandis can offer an excellent means for farming and buyers to trade in the post-COVID universe, at a moment when the entire world is disturbed by COVID-19 and social distance is the rule.
The e-mandi idea has already been attempted in several places in India, and it would improve the marketplace’s efficiency and transparency. As the state reshapes its economy after the present crisis, AI in Agri-Tech could return the farmer to his glorious days.
Use of Artificial Intelligence in Agriculture:
Cognitive technology is set to become the most transformative force in agriculture services because of its ability to learn, understand, and adapt to a range of circumstances to improve efficiency. Several of them could be producing services. Landowners could have a digital discussion on the platform and get immediate answers to their important questions. They also can keep track of the latest advancement that they should be aware of.
What AI brings to Agriculture
Artificial intelligence’s benefits in farming are obvious. Farm personnel can concentrate on more strategic operations that demand human intelligence by using smart farming technologies to automate mundane, repetitive, and time-consuming tasks. AI cannot be bought and started. Artificial intelligence is a group of technologies that are managed by software. Here are some benefits of Artificial Intelligence:
- Soil Analysis:
Proximity Sensing is two technologies that represent intelligent data synthesis. Soil testing is one application of this high-resolution data. Although remote sensing necessitates the integration of sensors into aerial or proximity sensing, satellite systems necessitate sensors that are in direct contact with the ground or at a very near range. This aids in the classification of soils depending on the soil under the surface at a specific location.
- Detecting Crop Diseases:
Computer Vision Technology is used to capture images of varied crops under (white/UV-A) light. Growers can then sort the produce into stacking before delivering it to the marketplace. The plant images are split into sections for further diagnosis thanks to image pre-processing. A method like this would allow pests to be identified more clearly.
- Crop Health:
When it comes to crop sowing, Artificial Intelligence is mostly used to power analytics that determines how and when to plant. It aids in the production of projections regarding the best period for planting, spraying soil nutrients, bales, harvesting, digging, etc., based on historical conditions, weather conditions, market conditions for outputs and inputs, personal information, and other considerations. Crops could also be sown at equidistant spacing and appropriate depths utilizing AI-assisted machinery.
- Monitoring Crop Health:
To develop crop metrics over thousands of acres, remote sensing methods, as well as 3D laser scanning, are required. It has the potential to usher in a fundamental shift in how landowners monitor crops in terms of time spent. This device will track crops throughout their whole life cycle and create reports to identify any irregularities.
- Weed and Pest Control:
Weed infections lower the overall crop output by up to 80 percent. Pests were also known for causing up to 20 percent of total losses. As a reason, pesticides kill more frequently, causing far more damage to the soil and water.
Drone-based photos can assist with in-depth field analysis, area scanning, crop monitoring, and other tasks. They can be used in conjunction with machine vision technologies and the Internet of Things to allow farmers to take quick actions. These streams can provide farmers with actual weather warnings.
- Crop Harvesting:
The hiring of labor provides for around 40 percent of yearly agriculture costs, primarily for harvesting and sowing. Harvesting with AI-enabled robots can save a lot of money by lowering the need for about four agricultural laborers per acre of land. Furthermore, plants can be sorted during harvest according to predetermined grades, reducing time and improving crop quality.
Conclusion: These instances of artificial intelligence in agriculture demonstrate the Indian government’s determination to promote social wealth through digital agriculture. While artificial intelligence in farming is still in its early stages in India, it has been heralded as a potential success story. In India, social responsibility has advanced in the field of smart farming. However, it has not sponsored several AI-driven Agri-projects as the other countries such as China and Brazil Indian businesses, particularly in the Agri-Tech industry, are appealing for CSR financing to help them realize their aim of turning deserts into productive farmlands. There is a very promising future for this technology in India.