Edge AI will transform the agriculture industry. In most cases, farmlands are localized where there is no availability of high-speed bandwidth, unappropriate resources to handle data, and it is also found that the farmers are not adequately educated about the best practices of agriculture.
In agriculture, it can be used as follows:
1. Soil quality: Examining the soil moisture using a mobile device by checking the farm location and the soil colour.
2. Milch animals’ health: Tracking the health of livestock by tagging the sensors will give the temperature, heart rate etc. and provide insights about the health condition.
3. Crop Health Analysis: A predictive computation engine, such as drones, can be used to check the health of leaves based on colour and the pores it has, whether attacked by insects, pests, or rodents.
4. Disaster protection: Using edge computing, agriculture IoT systems can make informed decisions about potential environmental hazards or natural disasters.
5. Examining leaves health: A predictive computation engine, such as drones, can be used to check the health of leaves based on colour and the pores it has, whether attacked by insects, pests, or rodents.
6. Analyzing satellite imagery: Deep analysis of satellite images provides an understanding of agricultural systems. With the help of Geo-spatial data, farmers can get information on crop distribution patterns across the globe along with the impact of weather changes on agriculture, among other applications.
7. Assess crop and soil heat: Predict the effect of different microbes on the health of plants and identify genetic changes that may cause due to harmful pathogens for the plant, among other things.
8. Predictive Analytics: Predictive models in AI help to do seasonal analysis, represent different market scenarios, and optimize business costs, among other applications.