How to Improve Computer Vision in AI for Precision Agriculture

Agriculture – the food generating sector is one of the leading occupations among the people in rural areas lacks due to underdeveloped methodologies or use of outdated know-how. But now AI in agriculture is boosting this sector using the power computer vision technology, to train the machines for better productivity in agro and farming.

Actually, high-labor cost and unavailability of such manual labor or increasing aesthetic standards for agricultural products, and greater global competition, encouraging farmers to adopt the latest automation technology to minimize their cost of production and improve the crop yield with better efficiency and margins in the markets.

AI companies can utilize the computer vision technology used in machine learning or deep learning in AI that can only help machines to recognize the various aspects in agricultural production and help farmers for precise farming.

In respect of the same, we brought here a great discussion what are the automated system, or how AI-based applications or machines can be trained and used to create computer vision-based AI model for agriculture and farming. And you can also find how AI companies can create the training data sets to train such models for this field.



Computer Vision in Robotics for Plant Detection

In agriculture a well-trained Robots can be used for performing various tasks like planting, weeding, harvesting and plant health detection. Such robots can detect plants, weeds and fruits or vegetables with the power of analyzing the health condition and fructify level to determine the harvesting time with the reaping capability of such crops.

To train the computer vision based AI model, annotated data in the format of images or pictures are used to make the subject or object of interest recognizable to machines through a machine learning algorithms for similar predictions.

And for there are multiple techniques to annotate the images for robotics used in agriculture and farming. To detect the crops, fruits and vegetables bounding box annotation is used to make these plants recognizable to machines.

Bounding box annotation can be used by AI companies to detect the plants, check the fructification level and recognize the unwanted plants or weeds. Bounding box annotation provides right inputs to computer vision for plants detection.

Computer Vision in Drones for Crop Monitoring

Drones are playing a crucial role in precise agriculture and farming. While flying in the midair, this autonomous flying object can capture the huge amount of data through camera installed for computer vision detection and training.

Drones can get the ariel view of entire field or cultivated ground and create a 3D map imagery that can viewed on computer screen from distance to monitor the health of crops or check soil condition through geosensing and visual sensing.

So, right here apart from bounding box, semantic segmentation image annotation and polygon annotation techniques are used to train the drones for mapping the agricultural fields and analyze the data for right forecasting.

Drone for Live Stock Management

Similarly, semantic segmentation is also used to make the animals recognizable from the midair making the AI possible in livestock management. A well-trained drone can recognize livestock, count them and monitor them without humans help.

Image annotations like bounding box technique also helps to detect and recognize livestock helping animal husbandry business operate with more efficiency for better productivity. In farming using the right algorithms computer vision based models are trained to detect the different types of animals without help of humans.

Yield Prediction Using Deep Learning

Apart from automated machines, the AI in agriculture can help by predicting the crop yield using the deep learning technology. Actually, deep learning with the help satellite imagery,various information can be gathered like soil conditions, nitrogen levels, moisture, seasonal weather and historical yield information of crops for precise farming.

And, using the deep learning technology AI software or application can be trained to analyze such things and that can be used on smartphones or tablets using the computer vision through the device camera to analyze the crops.

Computer Vision in Forestry

Computer vision technology is also used in autonomous machines like drones to analyze the aerial images of trees taken from heights, or by plane or satellite to monitor the deforestation activities and monitor the health condition of trees.

In forestry huge amounts of data is used to train the AI model to produce accurate measures, assessing the health and the growth of trees and enabling forest management professionals to take more accurate decisions.

Grading and Sorting of Crops

AI in computer vision for agriculture and farming can be also used to sort good crops from bad crops and determine which will be stable for longer shipments and which will go bad first and should be shipped to local markets.

Using the deep learning techniques once percentage of infection is calculated then on the basis of percentage do the Grading and sorting of the fruit image helping farmers to reduce the crop damages due to storage.

The right application of computer vision in agriculture is possible when the AI model is well-trained with annotated training data to make the varied objects or interest recognizable o machines. Anolytics is providing the image annotation services for computer vision based machine learning or deep learning model training.

So, if you are looking to develop a computer vision based AI model for agriculture and farming get in touch with Anolytics that can provide you the best quality of data sets at most affordable price while ensuring the accuracy at each stage.

Anolytics offers can annotate the images for varied AI models used in agriculture and farming. From robotics, to autonomous flying objects like drones, it can create the high-quality training data sets for computer vision in precise farming. It is working with well-trained annotators to annotate the images with best quality for accurate recognition by machines for right predictions.

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