Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting

Sensors

卷:20

Saeed Khaki¹  Lizhi Wang¹  Wade Kent²  Andy Kuhl²  Hieu Pham²  Ye Han² 

[1] Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011-3611, USA;

[2] Syngenta, Slater, IA 50244, USA;

关键词: corn kernel counting;    object detection;    convolutional neural networks;    digital agriculture;   

DOI  :  10.3390/s20092721

来源: DOAJ

【 摘 要 】

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 detects 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.

【 授权许可】

Unknown