How Advanced Technologies are Changing Agriculture and Farming


Agriculture is one of the world's oldest and most significant methods of cultivating crops and rearing livestock. With the aid of modern technology, we have gone a long way in terms of how we farm and cultivate new crops. Using data and technologies such as smart drones, satellite imagery, soil sensors, and so on, the agricultural sector has started witnessing positive results. But in order to achieve the desired outcomes from these machines, one has to train the algorithms with high-quality satellite and drone imagery datasets (training data).

Agriculture training datasets that are created by Anolytics and may be utilized in several areas of agriculture and farming. Various annotation approaches, such as bounding boxes, polygon annotation, semantic segmentation, cuboid annotation, key points, and polylines, are used to tackle jobs of any complexity.

Training Data for Aerial View Mapping of Fields 

Drone images can assist farmers with precision farming by providing in-depth crop monitoring, field scanning, and other services. These machines can provide real-time video observation and analysis of agricultural development trends.

The data collected by drones on farms is critical for making better agronomic decisions and is part of a system known as "precision agriculture."

Precision agriculture has embraced the usage of drones. Drone data assists farmers in achieving the highest potential harvests. Drones are frequently used to check the health of plants. This lets farmers monitor crops as they develop, allowing any issues to be addressed promptly. Apart from this, they can help in precise field mapping, including elevation data, so growers may spot any anomalies in their crops.

Drones may give important information of agricultural areas to check the soil quality using geo sensing and monitor the health of the crops, which is another sort of data you'll need for AI in agriculture. AI drones can monitor livestock such as cows, buffalos, lambs, and other animals used in animal husbandry in the farming industry. Drones can only learn from such data sets if they have access to such high-quality training data.

Intelligent machines are changing the scenario

Advanced machines can provide the most technologically accurate forecasts about the best crops to produce based on both fresh and old data from your farm as well as external elements such as weather reports.

Not only that, but these machines can detect pest illnesses and aid in speedier pest control. Even small company owners will be able to quickly connect with merchants all over the world because of artificial intelligence farms.

Data will be collected in the most time-efficient manner from each field in order to offer farmers a thorough, in-depth study that will give them a new perspective on their crops.

They will be able to decrease the cost of field oversight and respond faster to possible hazards like pests, severe rains, or dust storms by utilizing cutting-edge technologies such as edge computing.

Artificial intelligence will not only reveal inefficiencies in the present system but will also provide valuable new insights into how to address these issues.

Use Cases for Drone and Aerial Crop Monitoring

  • Plant Health Scouting/Monitoring
  • Observing the Situation
  • Seeding & Planting
  • Application of a Spray
  • Pollination using drones

EndNote

Digital farming has tremendous promise for agricultural growth, providing farmers with the tools they need to increase production and profitability. Despite the various advantages of digitalisation, farmers believe they are not reaping the benefits of the data gathered in their fields. It is critical to ensure that farmers are aware of their data rights and have access to important data in order to reap the advantages of better agricultural decision-making. 

Location: United States

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