Detection of spores using polarization image features and BP neural network

Yafei Wang, Ning Yang, Guoxin Ma, Mohamed Farag Taha, Hanping Mao, Xiaodong Zhang, Qiang Shi

Abstract


Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter’s direction was set to π/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse.
Keywords: greenhouse, spores, micropolarization image, BPNN, image processing, detection
DOI: 10.25165/j.ijabe.20241705.8873

Citation: Wang Y F, Yang N, Ma G X, Taha M F, Mao H P, Zhang X D, et al. Detection of spores using polarization image features and BP neural network. Int J Agric & Biol Eng, 2024; 17(5): 213-221.

Keywords


greenhouse, spores, micropolarization image, BPNN, image processing, detection

Full Text:

PDF

References


Yan H F, Zhao S, Zhang C, Zhang J Y, Wang G Q, Li M, et al. Calibration and assessment of evapotranspiration methods for cucumber plants in a Venlo-type greenhouse. Irrigation and Drainage, 2024; 73(1): 119–135.

Zhang C, Zhang W C, Yan H F, Ni Y X, Akhlaq M, Zhou J N, et al. Effect of micro-spray on plant growth and chlorophyll fluorescence parameter of tomato under high temperature condition in a greenhouse. Scientia Horticulturae, 2022; 306: 111441.

Lakhiar I A, Gao J M, Syed T N, Chandio F A, Tunio M H, Ahmad F, et al. Overview of the aeroponic agriculture - An emerging technology for global food security. Int J Agric & Biol Eng, 2020; 13(1): 1–10.

Lin J L, Ma J, Liu K, Huang X, Xiao L P, Ahmed S, et al. Development and test of an autonomous air-assisted sprayer based on single hanging track for solar greenhouse. Crop Protection, 2021; 142: 105502.

Yan H F, Deng S S, Zhang C, Wang G Q, Zhao S, Li M, et al. Determination of energy partition of a cucumber grown Venlo-type greenhouse in southeast China. Agricultural Water Management, 2023; 276: 108047.

Zhang C, Li X Y, Yan H F, Ullah I, Zuo Z Y, Li L L, et al. Effects of irrigation quantity and biochar on soil physical properties, growth characteristics, yield and quality of greenhouse tomato. Agricultural Water Management, 2020; 241: 106263.

Huang S, Yan H F, Zhang C, Wang G Q, Acquah S J, Yu J J, et al. Modeling evapotranspiration for cucumber plants based on the Shuttleworth-Wallace model in a Venlo-type greenhouse. Agricultural Water Management, 2020; 288: 105861.

Miao Y X, Luo X Y, Gao X X, Wang W J, Li B, Hou L P. Exogenous salicylic acid alleviates salt stress by improving leaf photosynthesis and root system architecture in cucumber seedlings. Scientia Horticulturae, 2020; 272: 109577.

Mao H P, Wang Y F, Yang N, Liu Y, Zhang X D. Effects of nutrient solution irrigation quantity and powdery mildew infection on the growth and physiological parameters of greenhouse cucumbers. Int J Agric & Biol Eng, 2022; 15(2): 68–74.

Shi Y, Yang Q Y, Zhao Q H, Dhanasekaran S, Ahima J, Zhang X Y, et al. Aureobasidium pullulans S-2 reduced the disease incidence of tomato by influencing the postharvest microbiome during storage. Postharvest Biology and Technology, 2022; 185: 111809.

Wallace E C, D’Arcangelo K N, Quesada-Ocampo L M. Population analyses reveal two host-adapted clades of Pseudoperonospora cubensis, the causal agent of cucurbit downy mildew, on commercial and wild cucurbits. Phytopathology Biology, 2020; 110: 1578–1587.

Riccardi C, Di Filipp.P, Pomata D, Simonetti G, Castellani F, Uccellitti D, et al. Comparison of analytical approaches for identifying airborne microorganisms in a livestock facility. Science of the Total Enviromnet, 2021; 783: 147044.

Shin M-Y, Viejo C G, Tongson E, Wiechel T, Taylor P W J, Fuentes S. Early detection of Verticillium wilt of potatoes using near-infrared spectroscopy and machine learning modeling. Computers and Electronics in Agriculture, 2023; 204: 107567.

Tang Z, Wang M N, Schirrmann M, Dammer K H, Li X R, Brueggeman R. Affordable high throughput field detection of wheat stripe rust using Deep Learning with Semi-Automated Image Labeling. Computers and Electronics in Agriculture, 2023; 207: 107709.

Tsai M-H, Li K-T. Leaf color as a morpho-physiological index for screening heat tolerance and improved water use efficiency in rabbiteye blueberry (Vaccinium virgatum Aiton). Sciental Horticulturae, 2021; 278: 109864.

Wang Y F, Mao H P, Xu G L, Zhang X D, Zhang Y K. A rapid detection method for fungal spores from greenhouse crops based on CMOS image sensors and diffraction fingerprint feature processing. Journal of Fungi, 2022; 8(4): 374.

Lei Y, Yao Z F, He D J. Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing. Scientific Reports, 2018; 8: 13647.

Yang N, Qian Y, EL-mesery H S, Zhang R B, Wang A Y, Tang J. Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree - confusion matrix method. Journal of the Science of Food and Agriculture, 2019; 99(14): 6589–6600.

Wang Y F, Du X X, Ma G X, Liu Y, Wang B, Mao H P. Classification methods for airborne disease spores from greenhouse crops based on multifeature fusion. Applied Sciences, 2020; 10(21): 7850.

Zhao Y C, Liu S G, Hu Z H, Bai Y, Shen C, Shi X Q. Separate degree based Otsu and signed similarity driven level set for segmenting and counting anthrax spores. Computers and Electronics in Agriculture, 2020; 169: 105230.

Zhang D Y, Zhang W H, Cheng T, Zhou X G, Yan Z H, Wu Y H, et al. Detection of wheat scab fungus spores utilizing the Yolov5-ECA-ASFF network structure. Computers and Electronics in Agriculture, 2023; 210: 107953.

Yang L, Chen W, Bi P S, Tang H Z, Zhang F J, Wang Z. Improving vegetation segmentation with shadow effects based on double input networks using polarization images. Computers and Electronics in Agriculture, 2022; 199: 107123.

Jiang W H, Lu J Q, Yang L V, Sa Y, Feng Y M, Ding J H, Hu Xinhua. Comparison study of distinguishing cancerous and normal prostate epithelial cells by confocal and polarization diffraction imaging. Journal of Biomedical Optics, 2015; 21(7): 071102.

Feng J W, Sa Y. A stain-free apoptosis detection and classification method based on machine learning technique. Chinese Journal of Cell Biology, 2019; 41(7): 1371–1376.

Feng Y M, Zhang N, Jacobs K M, Jiang W H, Yang L V, Li Z G, et al. Polarization imaging and classification of Jurkat T and Ramos B cells using a flow cytometer. Cytometry Part A, 2014; 85(9): 817–826.

Jia Z M, Liu F, Mu W, Wei G, Liu Y L. Study on the inoculation and fungicide sensitivity assay method of Sphaerotheca on cucumber. Journal of Plant Protection, 2006; 33(1): 99–103. (in Chinese)

Yu Z F, Li Y F, Deng L, Luo B, Wu P H, Geng D X. A high-performance cell-phone based polarized microscope for malaria diagnosis. Journal of Biophotonics, 2023; 16(5): e202200290.

Tong L, Huang X Y, Wang P, Ye L, Peng M, An L C, et al. Stable mid-infrared polarization imaging based on quasi-2D tellurium at room temperature. Nature Communications, 2020; 11(1): 2308.

Wang Y F, Zhang X D, Ma G X, Du X X, Shaheen N, Mao H P. Recognition of weeds at asparagus fields using multi-feature fusion and backpropagation neural network. Int J Agric & Biol Eng, 2021; 14(4): 190–198.

Liu H, Ji R H, Qi L J, Ma W, Gao C H. Spores of marigold black spot identification based on PCA and BP neural network. Journal of China Agricultural University, 2015; 20(6): 263–268. (in Chinese)




Copyright (c) 2024 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office