Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
| Autor: | Ye Han, Lizhi Wang, Wade Kent, Saeed Khaki, Hieu Pham, Andy Kuhl |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
FOS: Computer and information sciences Computer Science - Machine Learning food.ingredient digital agriculture Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Corn kernel Convolutional neural network Article Analytical Chemistry Machine Learning (cs.LG) Kernel (linear algebra) food Statistics - Machine Learning convolutional neural networks 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation business.industry Pattern recognition Regression analysis object detection Atomic and Molecular Physics and Optics Object detection Kernel (image processing) 020201 artificial intelligence & image processing Artificial intelligence corn kernel counting business Image based 010606 plant biology & botany |
| Zdroj: | Sensors, Vol 20, Iss 2721, p 2721 (2020) Sensors Volume 20 Issue 9 Sensors (Basel, Switzerland) |
| DOI: | 10.48550/arxiv.2003.12025 |
| Popis: | Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detect and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the (x,y) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles. Comment: 14 pages, 9 figures |
| Databáze: | OpenAIRE |
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