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

• URL: http://arxiv.org/abs/2003.12025v2
• Date: Mon, 20 Apr 2020 02:02:19 GMT
• Title: Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
• Authors: Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent and Lizhi Wang
• Abstract summary: A well developed corn ear can have up to 800 kernels. manually counting the kernels on an ear of corn is labor-intensive and prone to human error. We propose a kernel detection and counting method based on a sliding window approach.
• Score: 22.070850942573863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MaizeEar-SAM: Zero-Shot Maize Ear Phenotyping [0.9659487278134938]
  Grain yield per acre is calculated by multiplying the number of plants per acre, ears per plant, number of kernels per ear, and the average kernel weight. Traditional manual methods for measuring these two traits are time-consuming, limiting large-scale data collection. Our approach successfully identifies the number of kernels per row across a wide range of maize ears.
  arXiv  Detail & Related papers  (2025-02-19T03:18:29Z)
• Zonotope Domains for Lagrangian Neural Network Verification [102.13346781220383]
  We decompose the problem of verifying a deep neural network into the verification of many 2-layer neural networks. Our technique yields bounds that improve upon both linear programming and Lagrangian-based verification techniques.
  arXiv  Detail & Related papers  (2022-10-14T19:31:39Z)
• Neural Networks as Kernel Learners: The Silent Alignment Effect [86.44610122423994]
  Neural networks in the lazy training regime converge to kernel machines. We show that this can indeed happen due to a phenomenon we term silent alignment. We also demonstrate that non-whitened data can weaken the silent alignment effect.
  arXiv  Detail & Related papers  (2021-10-29T18:22:46Z)
• Random Features for the Neural Tangent Kernel [57.132634274795066]
  We propose an efficient feature map construction of the Neural Tangent Kernel (NTK) of fully-connected ReLU network. We show that dimension of the resulting features is much smaller than other baseline feature map constructions to achieve comparable error bounds both in theory and practice.
  arXiv  Detail & Related papers  (2021-04-03T09:08:12Z)
• WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting [12.735055892742647]
  We propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making. We call our model WheatNet and show that our approach is robust and accurate for a wide range of environmental conditions of the wheat field. Our proposed method achieves an MAE and RMSE of 3.85 and 5.19 in our wheat head counting task, respectively, while having significantly fewer parameters when compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-17T02:38:58Z)
• Blind Image Deblurring based on Kernel Mixture [0.0]
  This paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. A data-driven decision for the number of base kernels to combine makes the structure even more flexible.
  arXiv  Detail & Related papers  (2021-01-15T17:56:37Z)
• Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
  We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix. We train a CNN in the low-SNR regime to predict DoAs across all SNRs. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
  arXiv  Detail & Related papers  (2020-11-17T12:52:18Z)
• Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection [76.1522587605852]
  Isolation Distributional Kernel (IDK) is a new way to measure the similarity between two distributions. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection for both point and group anomalies.
  arXiv  Detail & Related papers  (2020-09-24T12:25:43Z)
• DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation [20.829106642703277]
  We propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data. DeepCorn estimates the density of corn kernels in an image of corn ears and predicts the number of kernels based on the estimated density map. Our proposed method achieves the MAE and RMSE of 41.36 and 60.27 in the corn kernel counting task, respectively.
  arXiv  Detail & Related papers  (2020-07-20T23:00:39Z)
• Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring [0.886014926770622]
  We present a novel, blind, single image deblurring method that utilizes information regarding blur kernels. We first introduce a kernel estimation network that produces adaptive blur kernels based on the analysis of the blurred image. We propose a deblurring network that restores sharp images using the estimated blur kernel.
  arXiv  Detail & Related papers  (2020-07-09T03:53:33Z)
• Learning Deep Kernels for Non-Parametric Two-Sample Tests [50.92621794426821]
  We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels parameterized by deep neural nets, trained to maximize test power.
  arXiv  Detail & Related papers  (2020-02-21T03:54:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.