Pest Detection on Plants Using Image Processing
DOI:
https://doi.org/10.32628/CSEIT25112516Keywords:
Pest detection, Disease classification, Machine learning, Image processing, Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM), Color segmentation, Agricultural technology, Precision farming, Computer visionAbstract
Plant pest and disease detection is crucial for ensuring agricultural productivity and food security. This paper presents a machine learning-based approach utilizing image processing techniques to identify pests and diseases in plants. The system employs Histogram of Oriented Gradients (HOG) for feature extraction and a Support Vector Machine (SVM) classifier for classification. The dataset is built from labelled images of pests and diseases in plants, and the trained model is used to predict new instances. Additionally, color-based segmentation in the HSV color space enhances detection by isolating affected regions. The method efficiently processes images, detects contours, and classifies the affected areas as either pest-infected or diseased. Experimental results demonstrate the model’s effectiveness in distinguishing between pests and diseases with high accuracy. The proposed system provides an automated and scalable solution for early detection, aiding in timely intervention and precision agriculture practices.
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Zhang, Y., et al. (2020). "Deep learning-based pest detection in agriculture: A review." Computers and Electronics in Agriculture, 170, 105251. (Referenced in Deep Learning (CNNs) method [1])
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). "Using deep learning for image-based plant disease detection." Frontiers in Plant Science, 7, 1419. (Referenced in Deep Learning (CNNs) method and accuracy comparison [2])
Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th Edition). Pearson. (Referenced in Thresholding & Segmentation techniques [3])
Jin, X., Che, J., & Wang, C. (2021). "HSV color space-based segmentation for plant disease detection." IEEE Access, 9, 126347-126356. (Referenced in Proposed Method using HSV segmentation [4])
Pantazi, X. E., Moshou, D., & Bochtis, D. (2019). "Advances in morphological filtering techniques for agricultural image processing." Biosystems Engineering, 183, 78-91. (Referenced in Morphological Filtering step [5])
Ferentinos, K. P. (2018). "Deep learning models for plant disease detection and diagnosis." Computers and Electronics in Agriculture, 145, 311-318. (Referenced in Deep Learning (CNNs) method for plant disease classification [6])
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). "Deep neural networks based recognition of plant diseases by leaf image classification." Computers and Electronics in Agriculture, 124, 176-182. (Referenced in CNN-based image classification [7])
Pydipati, R., Burks, T. F., & Lee, W. S. (2005). "Identification of citrus disease using color texture features and discriminant analysis." Computers and Electronics in Agriculture, 52(1-2), 49-59. (Referenced in color-based segmentation techniques [8])
Sharif, M., Khan, M. A., Iqbal, Z., Azam, M. F., & Lali, M. I. (2018). "Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection." Computers and Electronics in Agriculture, 150, 220-234. (Referenced in segmentation and feature extraction methods [9])
Barbedo, J. G. A. (2013). "Digital image processing techniques for detecting, quantifying, and classifying plant diseases." SpringerPlus, 2(1), 660. (Referenced in traditional image processing techniques [10])
Lu, J., Tan, L., Jiang, H., & Liu, Z. (2022). "A review on computer vision technologies applied in agricultural pest monitoring." Biosystems Engineering, 217, 33-49. (Referenced in computer vision approaches for pest detection [11])
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