International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Post-Harvest on Citrus Fruit Analyzing the Disease Type in Early Stages Using the Image Processing

Author : Lakshmi J V N ¹ Satya Siddharth Panda ²

Date of Publication :6th August 2022

Abstract: Image processing is a significant scientific tool for assessing food quality by using computer vision techniques. Plants are susceptible to diseases while practicing post-harvest technology. Detecting the diseases using the hyperspectral image segmentationtechnique by interpreting the external appearance and segmenting the diseased fruit is the current study. Particularly oranges the citrus fruits are highly vulnerable to post-harvest diseases such as brown rot, canker, scab, and greening due to high cold storage and also some of the pre-harvest factors. Classification of citrus typically orange fruit by identifying the disease by using the feature extraction by discovering different dimensions. Early detection of the diseases in the fruit prevents the fast spread and also reduces damage and financial loss. In the contemporary study on post-harvest disease detection in citrus fruits, a dataset of citrus diseased images is used and are easily classified with 79% of accuracy

Reference :

1. Ahmad, J., & Kamran, K. (2020). Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit. Food Science and Nutrition, 3346-3352.
2. Bouganis, B., & Shanahan, M. (2007). A vision based intelligent system for packing 2-D irregular shapes . IEEE Trans. Autom. Sci. Eng., 382-394.
3. Celebi, M., Kingravi, H., & Aslandogan, Y. (2007). Nonlinear vector filtering for impulsive noise removal from color images. Journal of Electronic Imaging.
4. Jose Antonio, A., Cynthia, G., Li, M., & Encarnacion, C. (2017). Image processing methods to evaluate tomato and Zucchini damage in post-harvest stages. International Journal Agricultutre and Biological Engineering, 126-133.
5. Kwok, N., Ha, Q., Liu, D., & Fang, G. (2009). IEEE trans. Autom. Sci. Eng. , 145-155.
6. Lee, D. J., & Archibald, J. K. (2011). Rapid color grading for fruit quality evlaution using direct color mapping. IEEE Trans. Autom. Sci. Eng. , 292-302.
7. Mohanaiah, P., Satyanarayana, P., & Gurukumar, L. (2013). Image Texture Feature Extraction Using GLCM Approach. International Journal of Scientifica and Research Publication, 1-11.
8. Ngan, H., & Pang, G. (2009). Regularity analysis for patterned texture inspection. IEEE Trans. Autom. Sci. Eng., 131-144.
9. Sergio, C., Won Suk, L., Nuria, A., Francisco, A., & Jose, B. (2016). Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review. Food Bioprocess Technol.
10. Zaragoza, A. (2010). Measurement of Colour of Citrus Fruits using an Automatic Computer Vision System