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IMAGE PRE-PROCESSING TECHNIQUES FOR CROP PEST DETECTION
Kh.M. Muraeva
Tashkent university of information technology
Abstract:
Pest detection systems are important tools for crop yields. Because
they serve as robust techniques while preventing some damages there. In this paper,
some image pre-processing techniques are discussed and efficient methods are
described.
Key words:
Image processing,
crops, pest management, detection, damage,
technologies, images.
Image processing plays a crucial role in crop pest detection due to the following
reasons:
•
Early detection
: Image processing enables early detection of crop pests, which
is crucial for effective pest management. By analyzing images of crops, it becomes
possible to identify signs of pest infestation at an early stage, allowing farmers to take
appropriate actions to mitigate the damage caused by pests.
•
Accuracy and efficiency
: Image processing algorithms can accurately and
efficiently analyze large amounts of crop images, identifying pests with high precision.
This helps in minimizing manual efforts and errors associated with traditional pest
detection methods, which are often labor-intensive and time-consuming.
•
Non-destructive approach
: In traditional pest detection methods, physical
sampling and inspection of crops may lead to damage, which negatively impacts the
productivity of crops. Image processing, on the other hand, provides a non-destructive
approach to pest detection. It allows farmers to assess the health of crops without
physically touching or harming them, thereby preserving their productivity and
minimizing any potential damage.
•
Remote sensing capabilities
: Image processing enables the use of remote
sensing technologies, such as satellites or drones, to capture images of large
20
agricultural areas. These images can then be processed to detect and monitor pests,
even in remote or inaccessible regions. This remote sensing capability provides a
broader perspective on pest distribution and helps farmers monitor and manage pest
infestations at a larger scale.
•
Integration with other technologies
: Image processing can be integrated with
other advanced technologies, such as artificial intelligence (AI), machine learning
(ML), and computer vision, to enhance pest detection accuracy. By training algorithms
with a large dataset of annotated pest images, AI and ML techniques can learn to
identify and classify pests accurately, improving the overall efficiency of crop pest
detection systems.
Figure 1: Crop pest detection system
Image preprocessing plays a crucial role in crop pest detection by enhancing the
quality and extracting relevant features from the images. Here are some common
preprocessing techniques used in crop pest detection:
•
Image resizing: Resizing the images to a standard size can help in reducing
computational complexity and ensuring uniformity in the dataset.
Figure 2: Image resizing process
•
Image denoising: Removing noise from images can improve the accuracy of
pest detection algorithms. There are a lot of techniques can be used for denoising:
1.
Gaussian blur: Apply a Gaussian filter to smooth out the image and reduce
noise.
2.
Median filtering: Replace each pixel's value with the median value of its
neighboring pixels to remove noise.
3.
Bilateral filtering: Preserve the edges while reducing noise by applying a
weighted average of neighboring pixels.
21
Figure 3: Image denoising
•
Image normalization: Normalizing the image intensities can help in
removing variations in lighting conditions across different images. Techniques like
histogram equalization or contrast stretching can be applied for normalization.
•
Image segmentation: Segmentation techniques can be used to separate the
foreground (crop and pests) from the background. This can be achieved through
thresholding, edge detection, or region-growing algorithms. Image segmentation for
crop pest detection refers to the process of dividing an image into multiple segments
or regions based on the presence of pests or abnormalities in the crops. This technique
helps identify and separate areas or instances of crop damage caused by pests from the
rest of the image. The process typically involves analyzing the image pixels and
applying various computer vision algorithms to separate the foreground (crop pests)
from the background (healthy crops). Some common methods for image segmentation
in crop pest detection include Thresholding, Region-based segmentation, Edge-based
segmentation, Deep learning based segmentation. By segmenting images into pest-
infested regions, farmers or pest control experts can focus their attention and resources
on these specific areas, enabling targeted intervention and minimizing the use of
pesticides or the spread of pests to healthy crops.
Figure 4: Segmentation process
•
Image enhancement: Enhancing the image features can make it easier to detect
pests. Techniques like sharpening, morphological operations, or adaptive histogram
22
equalization can be used for enhancing the image details. At this stage various
operation performed that are image resizing, filtering color space conversion and
histogram equalization. The size of images can be reduced using various algorithm like
as nearest-neighbor interpolation, box sampling, fourier transform method deep
convolution neural network.
Figure: Image enhancement process
•
Color space conversion: Converting the image to a different color space (e.g.,
RGB to HSV) can help in separating the pest regions based on color characteristics,
making it easier to detect them.
•
Data augmentation: Generating additional training images by applying
transformations like rotation, flipping, or scaling can help in improving the model's
performance and generalization.
Figure 6: Image augmentation process
Overall, image pre-processing plays a vital role in crop pest detection as it
enhances the quality of the images, improves visibility, reduces noise, and extracts
relevant features. These steps significantly contribute to the accuracy and efficiency of
the detection algorithms, allowing for timely and effective pest control measures.
References:
1.
Harshita Nagar, R.S Sharma “A comprehensive survey on pest detection
Technique using Image Processing” Proceedings of the International Conference on
Intelligent Computing and Control Systems (ICICCS 2020) IEEE Xplore Part
Number:CFP20K74ART; ISBN: 978-1-7281-4876-2.
2.
Neha Gautam., Nisha Chaurasia., Kunwar Pal, Digital image processing
application in Agriculture (Pest Detection) - Review paper
3.
Michael Mayo 1 and Anna T. Watson, Automatic Species Identification of
Live Moths, 2018.
