Abstract
Cotton plant is one of the most widely cultivated crop across worldwide. The leaf is one of the important parts which help in the food production. There are different cotton leaf diseases like Alternaria spot, foliar, bacterial blight, etc. which affects the agricultural yield. In order to detect the diseases, leaf region extraction becomes a significant task and to achieve this we use image processing techniques. Henceforth in this paper, a novel method used to extract the leaf region from a complex background. The proposed method is used for leaf extraction from complex background. The algorithm used in this method is modified factorization based active contour (MFACM) which helps in getting better output images. The database images used for research are acquired from the field using a digital camera. The proposed work is compared with existing active contour algorithms like Gradient Vector Flow (GVF), Adaptive Diffusion Flow (ADF), and Vector Flow Convolution (VFC). From the experiment, it can be observed that the proposed method is better than the other active contour methods in terms of computation time and the number of iterations. In addition to that segmented result is analyzed using specificity, sensitivity, precision which showed that our proposed method is better than the other methods.
Highlights
Plants play an important source of food for human beings
We proposed a leaf segmentation from complex background using modified factorization based active contour for texture segmentation
The proposed method and parametric deformable model methods Gradient Vector Flow (GVF), Adaptive Diffusion Flow (ADF), Vector Field Convolution (VFC) are experimented with dataset created by Nikon digital camera of (d) www.ijacsa.thesai.org
Summary
Plants play an important source of food for human beings. If plants get affected than yield will get affected. J Praveen kumar [3], introduced the new edge enhancement technique and graph-based method to extract the leaf region. Later it involves counting the number of leaves using circular Hough transform. The internal and external probability distribution functions are learned from a ground truth training set Using this segmentation is performed and it gave an outstanding result. Though there are various techniques used for the leaf segmentation [1][12][17][28] but with complex background very less research is carried out Taking this into consideration, we proposed a leaf segmentation from complex background using modified factorization based active contour for texture segmentation. The structure of the paper is as follows: Section II is Literature survey: discussed about previous work done; Section III is Methodology: explains regarding proposed method; Section IV is Results and discussion: the comparative study results; and Section V presents Conclusion
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