Detection of the yellow-leaf disease of rubber trees using low-altitude digital imagery from UAV

Jiangtao Qi, Mao Li, Huiming Zhang, Tiwei Zeng

Abstract


Efficient and non-destructive detection of rubber tree diseases is of great significance for optimizing disease control measures for pesticide application and fertilization. In this study, the feasibility of rubber yellow-leaf disease monitoring based on a low-altitude unmanned aerial vehicle (UAV) remote sensing platform was explored, and a low-cost method for detecting yellow-leaf disease based on visible light sensors was proposed. We compared the difference between the spectral response of each band of the visible light sensor in the diseased area and the healthy area, and then decorrelated and stretched the image in the RGB color space, thereby enhancing the color separation between highly correlated channels and enhancing the color difference of the image. Then we converted the image to the HSV color space, comparing the detection effect of different morphological parameters on yellow-leaf diseases and optimizing the extraction of the diseased area. The experimental results showed that this study provides the distribution information of yellow-leaf disease of rubber trees, and the R2 of the regression model of rubber trees was greater than 0.8. This study holds significance for optimizing disease control and sustainable development of therubber industry.
Keywords: rubber tree, yellow-leaf disease, low-altitude digital imagery, UAV
DOI: 10.25165/j.ijabe.20241706.9213

Citation: Qi J T, Li M, Zhang H M, Zeng T W. Detection of the yellow-leaf disease of rubber trees using low-altitude digital imagery from UAV. Int J Agric & Biol Eng, 2024; 17(6): 245–255.

Keywords


rubber tree, yellow-leaf disease, low-altitude digital imagery, UAV

Full Text:

PDF

References


Ge J, Wang X L, Koji K. Comparing tractive performance of steel and rubber single grouser shoe under different soil moisture contents. Int J Agric & Biol Eng, 2016; 9(2): 11–20.

Yoo H, Oh J, Chung W-J, Han H-W, Kim J-T, Park Y-J, et al. Measurement of stiffness and damping coefficient of rubber tractor tires using dynamic cleat test based on point contact model. Int J Agric & Biol Eng, 2021; 14(1): 157–164.

Puttipipatkajorn A, Puttipipatkajorn A. Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques. Int J Agric & Biol Eng, 2021; 14(3): 207–213.

Sabu T K, Vinod K V. Food preferences of the rubber plantation litter beetle, Luprops tristis, a nuisance pest in rubber tree plantations. Journal of Insect Science, 2009; 9(1): 72.

Tanzini M R, Alves S B, Tamai M A, De Moraes G J, Ferla N J. An epizootic of Calacarus heveae (Acari: Eriophyidae) caused by Hirsutella thompsonii on rubber trees. Experimental & applied acarology, 2000; 24(2): 141–144.

Rezende J M, Pereira J M, Araújo W S, Daud R D, Peres A J A. Population dynamics of rubber tree mites. Floresta e Ambiente, 2020; 27(4): 10.1590/2179–8087.017718.

Febbiyanti T R, Kusdiana A P J. Characteristics of rhizobacteria on healthy and white rot-infected rubber trees. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2020; 468: 012048.

Liyanage K K, Khan S, Mortimer P E, et al. Powdery mildew disease of rubber tree. Forest Pathology, 2016; 46(2): 90–103.

UN FAO. Rubber tapping. Available: https://www.fao.org/4/AD221E/AD221E06.htm. Accessed on [2024-10-24].

Rodrigo V H L. Adoption of different tapping systems in the rubber industry of Sri Lanka with special reference to low frequency tapping. 2007; 88: 1–21. doi: 10.4038/jrrisl.v88i0.1814

Qi D L, Zhu J L, Huang Y Q, Xie G S, Wu Z X. Factors affecting technology choice behaviour of rubber smallholders: a case study in central Hainan, China. Journal of Rubber Research, 2021; 24: 327–338.

Cheng X Z, Feng Y Y, Guo A T, Huang W J, Cai Z Y, Dong Y Y, et al. Detection of rubber tree powdery mildew from leaf level hyperspectral data using continuous wavelet transform and machine learning. Remote Sensing, 2023; 16(1): 105.

Kaewboonma N, Lertkrai P, Chanakot B, Lertkrai J. Thai rubber leaf disease classification using deep learning techniques. In: AICCC ’23: Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference, 2023; pp.84-91. doi:10.1145/3639592.3639605.

Li R J, Qin W B, He Y T, Li Y D, Ji R B, Wu Y H, et al. Method for the classification of tea diseases via weighted sampling and hierarchical classification learning. Int J Agric & Biol Eng, 2024; 17(3): 211–221.

Yin X, Li W H, Li Z, Yi L L. Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning. Int J Agric & Biol Eng, 2022; 15(3): 184–194.

Liang K, Ren Z Z, Song J P, Yuan R, Zhang Q. Wheat FHB resistance assessment using hyperspectral feature band image fusion and deep learning. Int J Agric & Biol Eng, 2024; 17(2): 240–249.

Eskandari R, Mahdianpari M, Mohammadimanesh F, Salehi B, Brisco B, Homayouni S. Meta-analysis of unmanned aerial vehicle (UAV) imagery for agro-environmental monitoring using machine learning and statistical models. Remote Sensing, 2020; 12(21): 3511.

Olson D, Anderson J. Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, 2021; 113(2): 971–992.

Yang G J, Liu J G, Zhao C J, Li Z H, Huang Y B, Yu H Y, et al. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Frontiers in Plant Science, 2017; 8: 1111.

Michalska-Pożoga I, Tomkowski R, Rydzkowski T, Thakur V K. Towards the usage of image analysis technique to measure particles size and composition in wood-polymer composites. Industrial Crops and Products, 2016; 92: 149–156.

Thorp K R, Dierig D A. Color image segmentation approach to monitor flowering in lesquerella. Industrial Crops and Products, 2011; 34(1): 1150–1159.

Tao X Y, Li Y J, Yan W Q, Wang M J, Tan Z F, Jiang J M, et al. Heritable variation in tree growth and needle vegetation indices of slash pine (Pinus elliottii) using unmanned aerial vehicles (UAVs). Industrial Crops and Products, 2021; 173: 114073.

Zhou D, Li M, Li Y, et al. Detection of ground straw coverage under conservation tillage based on deep learning. Computers and Electronics in Agriculture, 2020; 172: 105369.

Rastogi A, Arora R, Sharma S. Leaf disease detection and grading using computer vision technology & fuzzy logic. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida: IEEE, 2015; pp.500–505. doi: 10.1109/SPIN.2015.7095350.

Wspanialy P, Moussa M. A detection and severity estimation system for generic diseases of tomato greenhouse plants. Computers and Electronics in Agriculture, 2020; 178: 105701.

Liu L Y, Dong Y Y, Huang W J, Du X P, Ma H Q. Monitoring wheat fusarium head blight using unmanned aerial vehicle hyperspectral imagery. Remote Sensing, 2020; 12(22): 3811.

Deng J, Wang R, Yang L J, Lv X, Yang Z Q, Zhang K, et al. Quantitative estimation of wheat stripe rust disease index using unmanned aerial vehicle hyperspectral imagery and innovative vegetation indices. IEEE Transactions on Geoscience and Remote Sensing, 2023; 61: 1–11.

Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232.

Wang Y X, Xing M F, Zhang H G, He B B, Zhang Y. Rice false smut monitoring based on band selection of UAV hyperspectral data. Remote Sensing, 2023; 15(12): 2961.

Li N W, Huo L N, Zhang X L. Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images. Computers and Electronics in Agriculture, 2024; 217: 108665.

Corti M, Cavalli D, Cabassi G, et al. Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables. Precision Agriculture, 2019; 20(4): 675–696.

Kanning M, Kühling I, Trautz D, Jarmer T. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, 2018; 10(12): 2000.

Wang S W, Niu Y X, Ma X Y, Chen S L, Amani, Feng H. Prediction model for nitrogen content of rice leaves during heading stage in cold region based on hyperspectrum. Journal of Agricultural Mechanization Research, 2019; 41(3): 158–164. (in Chinese)

Mazlan S, JAAFAR N M, Wahab A, Sulaiman Z. Major diseases of rubber (Hevea brasiliensis) in Malaysia. Pertanika Journal of Scholarly Research Reviews, 2019; 5(2): 10–21.

Hamid N R A, Ghani Z A, Mahsuri I, Yusoff M A M, Rasib, A W, Yusoff A R M, et al. Rubber leaf disease detection from low altitude remote sensing techniques. Advanced Science Letters, 2018; 24(6): 4281–4285.

Serres E, Lacrotte R, Prévôt J, Clement A, Commere J, Jacob J. Metabolic aspects of latex regeneration in situ for three hevea clone. 1994; pp.79–88.

Cheng H, Tang C, Huang H. The Reyan 7-33-97 rubber tree genome: insight into its structure, composition and application. The Rubber Tree Genome, 2020: 13–40.

Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012; 34(11): 2274–2282.

Swain M J, Ballard D H. Color indexing international journal of computer vision, 1991; 7(1): 11–32.

Lu S Y, Wang B Z. An image retrieval algorithm based on improved color histogram. In: Journal of Physics: Conference Series. IOP Publishing, 2019; 1176(2): 022039.

Lee H S, In Cho S. Spatial color histogram-based image segmentation using texture-aware region merging. Multimedia Tools and Applications, 2022; 81(17): 24573–24600.




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