Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image
Keywords:Agriculture, Leaf, Precision, Segmentation, Sobel.
The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.
Sharma, S. & Gupta, A., A Review for the Automatic Methods of Plant's Leaf Image Segmentation, International Journal of Intelligence Sustainable Computing, 1(3), pp. 101-114, 2020.
Nikbakhsh, N., Baleghi, Y. & Agahi, H., A Novel Approach for Unsupervised Image Segmentation Fusion of Plant Leaves Based on G-Mutual Information, Machine Vision and Applications 32(5), 2021.
Buoncompagni, S. Maio, D. & Lepetit, V., Leaf Segmentation under Loosely Controlled Conditions, in BMVC, pp. 133.1-133.12, 2015.
Khan, R. & Debnath, R., Segmentation of Single and Overlapping Leaves by Extracting Appropriate Contours, Information Processing in Agriculture, 2019.
Bello-Salau, H., Aibinu, A.M., Onwuka, E.N., Dukiya, J.J. & Onumanyi, A.J., Image Processing Techniques for Automated Road Defect Detection: A Survey, in 2014 11th International Conference on Electronics, Computer and Computation (ICECCO), pp. 1-4, IEEE, 2014.
Manuel, G.B., Vacavant, A., Cerutti, G., Kurtz, V., Weber, J. & Tougne, L., Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods, IEEE Transactions on Image Processing, 24(5), pp. 1549-1560, 2015.
Kaur, M. & Goyal, P., A Review on Region Based Segmentation, International Journal of Science Research, 4(4), pp. 3194-3197, 2015.
Bello-Salau, H., Onumanyi, A.J., Salawudeen, A.T., Mu'azu, M.B. & Oyinbo, A.M., An Examination of Different Vision based Approaches for Road Anomaly Detection, in 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), pp. 1-6: IEEE, 2019.
Chopin, J., Laga, H. & Miklavcic, S., A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves, PloS One, 11(12), pp. e0168496, 2016.
Chen, Y., Baireddy, S., Cai, E., Yang, C. & Delp, E., Leaf Segmentation by Functional Modeling, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019.
Tian, K., Li, J., Zeng, J., Evans, A. & Zhang, L., Segmentation of Tomato Leaf Images Based on Adaptive Clustering Number of K-Means Algorithm, Computers Electronics in Agriculture, 165, 104962, 2019.
Chong, N., Han, L., Yuguang, N., Zengchan, Z., Yunlong, B. & Wengang, Z., Segmentation of Cotton Leaves Based on Improved Watershed Algorithm, in International Conference on Computer and Computing Technologies in Agriculture, pp. 425-436. Springer, Cham, 2015.
Peng. W., Wenlin, L. & Wenlong, S., Segmentation of Leaf Images Based on the Active Contours, International Journal of u- and e- Service, Science and Technology, 8, pp. 63-70, 2015.
Cerutti, G., Tougne, L., Mille, J., Vacavant, A. & Coquin, D., Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification, Computer Vision Image Understanding, 117(10), pp. 1482-1501, 2013.
Morris, D., A Pyramid CNN for Dense-Leaves Segmentation, in 2018 15th IEEE Conference on Computer and Robot Vision (CRV), pp. 238-245, 2018.
Xia, C., Wang, L., Chung, B.K. & Lee, J.M., In Situ 3D Segmentation of Individual Plant Leaves Using A RGB-D Camera for Agricultural Automation, Sensors, 15(8), pp. 20463-20479, 2015.
Wang, Z., Wang, K., Yang, F., Pan, S. & Han, Y., Image Segmentation of Overlapping Leaves Based On Chan-Vese Model and Sobel Operator, Information Processing in Agriculture, 5(3), pp. 1-10, 2018.
Wang, J., He, J., Han, Y., Ouyang, C. & Li, D., An Adaptive Thresholding Algorithm of Field Leaf Image, Computers Electronics in Agriculture, 96, pp. 23-39, 2013.
Zhang, L., Weckler, P., Wang, N., Xiao, D. & Chai, X., Individual Leaf Identification from Horticultural Crop Images Based on the Leaf Skeleton, Computers Electronics in Agriculture, 127, pp. 184-196, 2016.
Meyer, G.E. & Neto, J.C., Verification of Color Vegetation Indices for Automated Crop Imaging Applications, Computers Electronics in Agriculture, 63(2), pp. 282-293, 2008.
Dass, R., Devi, S. & Priyanka, Image Segmentation Techniques, International Journal of Electrical & Communication Technology, 3(3), pp. 66-70, 2012.
Lomte, S.S. & Janwale, A.P., Plant Leaves Image Segmentation Techniques: A Review, International Journal of Computer Sciences and Engineering, 5(5), pp. 147-150, 2017.
Liu, S. & Peng, Y., A Local Region-Based Chan-Vese Model for Image Segmentation, Pattern Recognition, 45(7), pp. 2769-2779, 2012.
Adam, S. & Arifin, A.Z., Separation of Overlapping Object Segmentation Using Level Set with Automatic Initialization on Dental Panoramic Radiograph, Jurnal Ilmu Komputer dan Informasi, 13(3), pp. 25-34, 2020.
Lang, Y. & Zheng, D., An Improved Sobel Edge Detection Operator, in 2016 6th International Conference on Mechatronics, Computer and Education Information (MCEI 2016), Atlantis Press, 2016.
Yuan, T., Huixian, H., Jianmin, X. & Ren, C., Road Edge Detection from Remote Sensing Image Based On Improved Sobel Operator, Remote Sensing for Land Resources, 28(3), pp. 7-11, 2016.
Fawcett, T., Introduction to ROC Analysis, Pattern Recognition Letters, 27(8) pp. 861-74, 2006.
Patel, R., Mewada, H. & Patnaik, S., Fast and Regularization Less Active Contour, International Journal of Computer Applications, 975, 888, 2012.