Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image


  • Jibrin Bala Ahmadu Bello University
  • Habeeb Bello Salau Ahmadu Bello University
  • Ime Jarlath Umoh Ahmadu Bello University
  • Adeiza James Onumanyi Federal University of Technology Minna
  • Salawudeen Ahmed Tijani University of Jos
  • Basira Yahaya Ahmadu Bello University



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.

Author Biographies

Jibrin Bala, Ahmadu Bello University

Department of Computer Engineering

Postgraduate student (MSc.  Computer Engineering)

Habeeb Bello Salau, Ahmadu Bello University

Department of Computer Engineering 

Senior Lecturer

Ime Jarlath Umoh, Ahmadu Bello University

Department of Computer Engineering

Senior Lecturer

Adeiza James Onumanyi, Federal University of Technology Minna

Department of Telecommunication Engineering 

Senior Lecturer

Salawudeen Ahmed Tijani, University of Jos

Department of Electrical and Electronic Engineering 

Senior Lecturer

Basira Yahaya, Ahmadu Bello University

Department of Computer Engineering 



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.