Application of Computer Vision in Precision Agriculture & Farming
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is one of the leading occupations among the people in rural areas lacks due to underdeveloped methodologies or use of outdated know-how”.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 of agricultural production and help farmers for precise farming.
In respect of the same, we brought here a great discussion about what is the automated system, or how AI-based applications or machines can be trained and used to create a 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.
Application of Computer Vision in Agriculture
In agriculture, AI in robotics can help to perform 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 machine learning algorithms for similar predictions.
And 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 the right inputs to computer vision for plant 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 a huge amount of data through a camera installed for computer vision detection and training.
AI-enabled drones can get the ariel view of the entire field or cultivated ground and create a 3D map imagery that can be viewed on a computer screen from distance to monitor the health of crops or check soil conditions through geosensing and visual sensing.
Video: Computer Vision in Crop Monitoring through Drones
So, right here apart from the 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 the right forecasting.
Computer Vision for Livestock 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 human’s help.
Image annotations like the bounding box technique also help to detect and recognize livestock helping animal husbandry businesses 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 the help of humans.
Computer Vision for Yield Prediction Using Deep Learning
Apart from automated machines, the AI in agriculture can help by predicting the crop yield using deep learning technology.
Actually, deep learning with the help of 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 Management
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 are used to train the AI model to produce accurate measures, assessing the health and the growth of trees and enabling forest management professionals to make more accurate decisions.
Computer Vision for Spraying Pesticides on Crops
The AI-enabled drones are capable to monitor the infected crops and spray the pesticides to prevent crops from insects and pests. The computer vision allows drones to precisely detect the infected crops and spray the pesticides accordingly. And further with more improved vision power of a computer, more precision will protect crops.
Video: Drones Spraying Pesticides on Crops:
Computer Vision in 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 the 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 interests recognizable o machines. Anolytics is providing the image annotation services for computer vision-based machine learning or deep learning model training.
Video: Sorting of Fruit using Machine Learning
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 the most affordable price while ensuring accuracy at each stage.
This story was originally featured on www.vsinghbisen.com
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