20240013363. SYSTEM AND METHOD FOR MEASURING LEAF-TO-STEM RATIO simplified abstract (Deere & Company)
SYSTEM AND METHOD FOR MEASURING LEAF-TO-STEM RATIO
Organization Name
Inventor(s)
Mahesh Somarowthu of Pune (IN)
SYSTEM AND METHOD FOR MEASURING LEAF-TO-STEM RATIO - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240013363 titled 'SYSTEM AND METHOD FOR MEASURING LEAF-TO-STEM RATIO
Simplified Explanation
The patent application describes systems and methods for automatically determining the leaf-to-stem ratio of a crop from an image of the crop. An agricultural vehicle is equipped with a crop analysis system that includes a computing device and an imaging device. The imaging device captures an image of the crop, and the computing device analyzes the acquired images to determine the leaf-to-stem ratio of the crop.
- The innovation involves using an imaging device on an agricultural vehicle to capture images of crops.
- The computing device then analyzes these images to determine the leaf-to-stem ratio of the crop.
- The leaf-to-stem ratio is an important metric in agriculture as it provides insights into the health and growth of the crop.
- This technology eliminates the need for manual measurement and analysis of the leaf-to-stem ratio, saving time and effort for farmers.
- The system can be integrated into existing agricultural vehicles, making it easily accessible and cost-effective.
Potential Applications
This technology has various potential applications in the agricultural industry:
- Crop management: Farmers can use the leaf-to-stem ratio data to optimize their crop management practices, such as adjusting irrigation, fertilization, and pest control strategies.
- Yield prediction: The leaf-to-stem ratio can be used as an indicator of crop yield potential, allowing farmers to make informed decisions regarding harvesting and marketing.
- Disease detection: Changes in the leaf-to-stem ratio can indicate the presence of diseases or stress in crops, enabling early detection and targeted intervention.
- Research and development: Researchers can utilize this technology to study the effects of different factors on crop growth and development, leading to advancements in agricultural science.
Problems Solved
This technology addresses several problems in crop analysis:
- Manual labor: Traditional methods of measuring the leaf-to-stem ratio require manual sampling and analysis, which is time-consuming and labor-intensive.
- Inaccuracy: Human error in manual measurements can lead to inconsistent and unreliable results.
- Limited coverage: Manual sampling only provides a limited representation of the entire crop, whereas this technology can capture images of the entire field, providing a more comprehensive analysis.
- Real-time monitoring: The automated nature of this technology allows for real-time monitoring of the leaf-to-stem ratio, enabling prompt decision-making and intervention.
Benefits
The use of this technology offers several benefits:
- Efficiency: The automated analysis of crop images eliminates the need for manual measurements, saving time and effort for farmers.
- Accuracy: By removing human error, the technology provides more accurate and consistent measurements of the leaf-to-stem ratio.
- Cost-effectiveness: Integrating the system into existing agricultural vehicles reduces the need for additional equipment, making it a cost-effective solution.
- Data-driven decision-making: The leaf-to-stem ratio data can inform farmers' decisions regarding crop management, leading to improved productivity and resource utilization.
- Early detection: The ability to detect changes in the leaf-to-stem ratio allows for early identification of crop diseases or stress, enabling timely intervention and minimizing crop loss.
Original Abstract Submitted
systems and methods are provided for determining a leaf-to-stem ratio of a crop automatically from an image of the crop. an agricultural vehicle may include a crop analysis system having a computing device and an imaging device for capturing an image of a crop. the computing device analyzes images acquired by the imaging determine to determine a leaf-to-stem ratio of the crop.
