International business machines corporation (20240160916). ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD simplified abstract
Contents
- 1 ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD
Organization Name
international business machines corporation
Inventor(s)
Sathya Santhar of Ramapuram (IN)
Sridevi Kannan of Chennai (IN)
Sarbajit K. Rakshit of Kolkata (IN)
ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160916 titled 'ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD
Simplified Explanation
The abstract describes a method, computer system, and computer program product for flora yield prediction. The process involves identifying various florae, their locations, attributes, generating a neural network model, calculating tensors for cross-pollination, and performing cross-pollination based on the calculated tensors.
- Identifying a plurality of florae and their locations within a preconfigured space
- Identifying attributes of each flora
- Generating a neural network model based on the identified florae, locations, and attributes
- Calculating tensors from anthers to stigmas of each flora based on the neural network model
- Performing cross-pollination based on the calculated tensors
Potential Applications
This technology could be applied in agriculture for optimizing crop yield by predicting and enhancing flora yield through cross-pollination.
Problems Solved
This technology helps in predicting flora yield accurately, optimizing cross-pollination processes, and improving overall agricultural productivity.
Benefits
The benefits of this technology include increased crop yield, efficient use of resources, and improved agricultural practices.
Potential Commercial Applications
One potential commercial application of this technology could be in the agricultural industry for enhancing crop yield and productivity.
Possible Prior Art
One possible prior art could be traditional methods of cross-pollination in agriculture, which may not be as efficient or accurate as the neural network-based approach described in this patent application.
Unanswered Questions
How does this technology compare to existing methods of flora yield prediction?
The article does not provide a direct comparison to existing methods of flora yield prediction, making it unclear how this technology stands out in the field.
What are the specific attributes of florae that are considered in the neural network model?
The article does not specify the exact attributes of florae that are used in the neural network model, leaving a gap in understanding the input variables of the prediction process.
Original Abstract Submitted
according to one embodiment, a method, computer system, and computer program product for flora yield prediction is provided. the embodiment may include identifying a plurality of florae and a location of each flora within the plurality of florae within a preconfigured space. the embodiment may also include identifying one or more attributes of each flora. the embodiment may further include generating a neural network model based on the plurality of florae, the location of each flora, and the one or more identified attributes. the embodiment may also include calculating tensors from an anther of each flora to one or more stigmas of each other flora within the plurality of florae based on the generated neural network model. the embodiment may further include performing cross-pollination of the plurality of florae based on the calculated tensors.
