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AI Enhancing Effectiveness in Agriculture Through Monitoring Clouds

  /  Cloud   /  AI Enhancing Effectiveness in Agriculture Through Monitoring Clouds

AI Enhancing Effectiveness in Agriculture Through Monitoring Clouds

AI-based cloud monitoring techniques are helping farmers enhance effectiveness in agriculture.

The climate changes are irreversible, and the backbone of India, i.e., agriculture is the most affected. AI improves efficiency and manages the challenges in all the fields and also in the agricultural sector like the crop yield, irrigation, soil content sensing, crop- monitoring, and so much more. AI can tell how the weather will be in the next two hours because we can’t change the weather but we can predict and monitor it. The AI weather forecasting can make more accurate short-term predictions, including critical storms and floods. AI cloud monitoring technology in agriculture can help save crops in the long run. Climate change is making it harder to anticipate adverse weather conditions, as the frequency and severity of heavy rain increases, it will lead to crop damage and losses.


Cloud Monitoring System for Successful Agriculture Forecasting:

AI weather forecasting is helping farmers to improve decision-making and crop production. AI-based weather forecasting done by IBM’s Watson Decision Platform is helping farmers across the country to increase their profitability and yield more tons of produce per hectare. Three primary technologies act to the development of efficient cloud monitoring for agriculture, and those are:

Satellite and Hardware Stations: Farmers should adopt satellite information and utilize aerial images to view crop yields and carry out weather forecasting. Here, satellites can be utilized in data sources, data transmitters. AI in weather monitoring systems allows farmers to utilize satellites to monitor excess meteorological and geospatial data to make decisions for severe uncommon weather.

Smart IoT Sensors: IoT sensors play the base for a larger connected system for weather forecasting in agriculture. This depends on a network of connected sensors that accumulate data within the field. Utilizing this, farmers can access climate changes information on the soil and environment to plan actions way ahead. When a weather monitoring system receives disturbing information from IoT sensors, it can transfer notification of forthcoming rainfall.

AI and Machine Learning:  AI and machine learning to forecasting weather is an advanced technology for agriculture. It can be crowd-sourced from connected satellites sensors and local hardware weather stations to give rise to accurate weather predictions.

The concept of developing AI cloud monitoring in agriculture can mitigate the effects of weather change by predicting and expected weather that ends up harming crops. Predicting climate changes will empower agriculture to optimize resources, harvest, and produce safe crops.