18055877. ARTIFICIAL INTELLIGENCE-ENABLED CROSS POLLINATION FOR PREDICTIVE YIELD simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
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
- 1.9.1 Unanswered Questions
- 1.9.2 How does this technology compare to traditional methods of cross-pollination and plant breeding in terms of efficiency and effectiveness?
- 1.9.3 What are the potential limitations or challenges in implementing this technology on a large scale in agricultural settings?
- 1.10 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 18055877 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, involving identifying florae, their locations, attributes, generating a neural network model, calculating tensors for cross-pollination.
- Identifying 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 anther to stigma 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 and improving flora yield, leading to increased agricultural productivity and efficiency.
Benefits
The technology enables better utilization of resources, increased crop yield, and potentially higher profits for farmers.
Potential Commercial Applications
The technology can be used in the agricultural industry for precision farming, seed production, and crop improvement, leading to more sustainable and profitable farming practices.
Possible Prior Art
One possible prior art could be traditional methods of cross-pollination and plant breeding techniques used in agriculture.
Unanswered Questions
How does this technology compare to traditional methods of cross-pollination and plant breeding in terms of efficiency and effectiveness?
This article does not provide a direct comparison between this technology and traditional methods of cross-pollination and plant breeding.
What are the potential limitations or challenges in implementing this technology on a large scale in agricultural settings?
The article does not address the potential limitations or challenges in implementing this technology on a large scale in agricultural settings.
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.
