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Impact of Artificial Intelligence on Agriculture

Food and Beverages | Friday, December 31, 2021

Crop yield prediction is critical for the global food production ecosystem. Thanks to AI, it becomes possible to make educated decisions with better facts in hand.

Fremont, CA: The majority of human activities are being reshaped by technology. Agriculture is no exception. As newer crop and farm management approaches become more popular, concepts like Smart Farming have gained traction. It is transforming farming into a more efficient and profitable business.

There are several factors to consider when it comes to crop yield forecasting. Climate data, satellite photos, soil conditions, and insect attack potential are just a few of them. These factors come together to provide a comprehensive picture of the best times for crop production. Like any other industry, agriculture is benefiting from AI by building a platform for real-time analysis of factors such as weather and soil conditions. Sensors check for things like moisture content and pH level in the soil. A companion app provides a comprehensive tour of various portions of the field and crop health.

AI is having a positive impact on agriculture in the following ways:

Automated monitoring of soil and crops

Since 1972, LANDSAT data has aided with agricultural monitoring. LANDSAT imagery is critical for calculating crop productivity and monitoring water use, among other things. They're also useful for field-level management, such as identifying different conditions and increasing yield by using zone mapping.

Health analysis of crops

Drone-based images data can be used to monitor and determine crop health using GeoSpatial AI analysis. Drones collect data in the field and transmit it to computers for analysis. Algorithms in the system evaluate photos to determine farm health. The advantage is that pests can be identified, and mitigating steps can be taken to solve the problem.

Weather forecasting

Weather prediction is achievable using IoT-based sensors and historical data. Past data will always have a pattern, which the machine will use to forecast future weather. Farmers will benefit since they will be able to choose a favorable time to plant seeds and collect the crop.

Predictive analytics

In recent years, it has become a critical component of precision farming. Crop rotation, water management, pest infestations, nutrition management, and much more may all be studied using IoT devices. The gadgets then use geographical analysis to generate rich insights that help enhance agricultural production standards.

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