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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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The important convolution properties include width, area, differentiation, and integration properties.
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Convolution computations can be simplified by utilizing their inherent properties.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting.

Saeed Khaki1, Hieu Pham2, Ye Han2

  • 1Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011-3611, USA.

Sensors (Basel, Switzerland)
|May 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for counting corn kernels using a sliding window approach and convolutional neural networks (CNNs). This technology improves yield estimation accuracy for farmers, reducing manual labor and potential profit loss.

Keywords:
convolutional neural networkscorn kernel countingdigital agricultureobject detection

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate in-season corn yield estimation is crucial for farmers' profitability, aiding harvest and marketing decisions.
  • Manual corn kernel counting is laborious, time-consuming, and susceptible to human error.
  • Detecting numerous, closely spaced kernels at various angles in images presents a significant algorithmic challenge.

Purpose of the Study:

  • To develop and evaluate an automated method for detecting and counting corn kernels from single ear images.
  • To address the challenges of kernel detection in uncontrolled lighting and varying angles.
  • To provide a more efficient and accurate alternative to manual kernel counting for yield estimation.

Main Methods:

  • A sliding window approach combined with a convolutional neural network (CNN) was employed for kernel detection.
  • Non-maximum suppression (NMS) was utilized to eliminate redundant overlapping detections.
  • A secondary CNN regression model identified the precise coordinates of detected kernel centers.

Main Results:

  • The proposed method successfully detected corn kernels with a low error rate.
  • The system demonstrated effectiveness in detecting kernels across multiple corn ears positioned at different angles.
  • The approach proved robust even under uncontrolled lighting conditions.

Conclusions:

  • The developed kernel detection and counting method offers a viable, automated solution for corn yield estimation.
  • This approach significantly reduces the labor and time associated with manual counting.
  • The method's accuracy and adaptability to different conditions support its practical application in agriculture.