Optimized Feature Extraction Model on RCNN and BPNN Models for Grape Disease Detection


  • Prasad P. S., Blessed Prince P.


Deep Learning, Grape Leaf , Disease, Prediction , R-CNN, BPNN


One of the most pressing problems facing farmers today is the proliferation of plant diseases, which pose a serious threat to the safety of the food we consume. Therefore, it is crucial to detect these diseases early and find viable treatments to prevent them. This study analyses numerous methods for diagnosing and classifying diseases that might affect grapevines. The aim of this research is to provide a thorough overview of the many techniques used to identify and categorise grape leaf diseases. Important image processing procedures for disease prediction are discussed, including picture collection, data pre-processing, image segmentation, feature extraction, and image classification. Convolution Neural Network (R-CNN and BPNN are only some of the standard image processing and detection and classification methods covered. To better assist researchers in determining which methods can be selected to enhance grape leaf disease identification and classification efficiency, we have identified the differences caused by deep learing techniques and the various processes used to obtain various results by referencing a number of articles.



Download data is not yet available.


. Suyash S. Patil, Sandeep A. Thorat. “Early Detection of Grapes Diseases Using Machine Learning and IOT”, 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) [4] Nitesh Agrawal, Jyoti Singhai, Dheeraj K. Agrawal, “Grape Leaf Disease Detection and classification Using International Journal of Computer Applications (0975 – 8887)Volume 178 – No. 20, June 2019

. Multi-class Support Vector Machine”, Proceeding International conference on Recent Innovations is Signal Processing and Embedded Systems (RISE-2017) 27-29October,2017.

. Harshal Waghmare, Radha Kokare, “ Detection and Classification of Diseases of Grape Plant Using Opposite Colour Local Binary Pattern Feature and Machine Learning for Automated Decision Support System”, 2016

. 3rd International Conference on Signal Processing and Integrated Networks (SPIN)

. Hulya Yalcin, “Plant Phenology Recognition using Deep Learning : Deep-Pheno”

. Emanuel Cortes, “Plant Disease Classification Using Convolutional Networks and Generative Adversial Networks”.

. I.Gogul, V.Sathiesh Kumar, “Flower Species Recognition System using Convolutional Neural Networks and Transfer Learning”, 2017 4th International Conference on Signal Processing, Communications and Networking (ICSCN -2017), March 16 – 18, 2017, Chennai, INDIA

. R. Chand, P. Joshi, and S. Khadka, Indian Agriculture Towards 2030: Pathways for Enhancing Farmers’ Income, Nutritional Security and Sustainable Food and Farm Systems. Springer Nature, 2022.

. A. and P. F. P. E. D. A. of INDIA, “GRAPES RTI,” Ministry of Commerce and Industry, Government of India, 2021. https://apeda.gov.in/apedawebsite/SubHead_Products/Grapes.htm#:~:text=India Facts and Figures %3A,fresh Grapes to the world.

. B. Sandika, S. Avil, S. Sanat, and P. Srinivasu, “Random forest based classification of diseases in grapes from images captured in uncontrolled environments,” in 2016 IEEE 13th International Conference on Signal Processing (ICSP), 2016, pp. 1775–1780. doi: 10.1109/ICSP.2016.7878133.

. R. Shashidhar, A. S. Manjunath, R. Santhosh Kumar, M. Roopa, and S. B. Puneeth, “Vehicle Number Plate Detection and Recognition using YOLO- V3 and OCR Method,” in 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), 2021, pp. 1–5. doi: 10.1109/ICMNWC52512.2021.9688407.

. M. Ji and Z. Wu, “Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic,” Comput. Electron. Agric., vol. 193, p. 106718, 2022, doi: https://doi.org/10.1016/j.compag. 2022. 106718.

. S. M. Javidan, A. Banakar, K. A. Vakilian, and Y. Ampatzidis, “Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning,” Smart Agric. Technol., vol. 3, p. 100081, 2023, doi: https://doi.org/10.1016/j.atech.2022.100081.

. U. Sanath Rao et al., “Deep Learning Precision Farming: Grapes and Mango Leaf Disease Detection by Transfer Learning,” Glob. Transitions Proc., vol. 2, no. 2, pp. 535–544, 2021, doi: https://doi.org/10.1016/j.gltp.2021.08.002.

. Z. Tang, J. Yang, Z. Li, and F. Qi, “Grape disease image classification based on lightweight convolution neural networks and channelwise attention,” Comput. Electron. Agric., vol. 178, p. 105735, 2020

. M. Ji, L. Zhang, and Q. Wu, “Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks,” Inf. Process. Agric., vol. 7, no. 3, pp. 418–426, 2020.

. A. Adeel et al., “Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion,” Sustain. Comput. Informatics Syst., vol. 24, p. 100349, 2019.

. N. R. Kolhalkar and V. L. Krishnan, “Mechatronics system for diagnosis and treatment of major diseases in grape vineyards based on image processing,” Mater. Today Proc., vol. 23, pp. 549–556, 2020.

. M. Harahap, V. Angelina, F. Juliani, and O. Evander, “Grape disease detection using dual channel Convolution Neural Network method,” Sink. J. dan Penelit. Tek. Inform., vol. 5, no. 2, pp. 314–324, 2021.

. J. Zhu, A. Wu, X. Wang, and H. Zhang, “Identification of grape diseases using image analysis and BP neural networks,” Multimed. Tools Appl., vol. 79, no. 21, pp. 14539–14551, 2020.

. R. Dwivedi, S. Dey, C. Chakraborty, and S. Tiwari, “Grape disease detection network based on multi-task learning and attention features,” IEEE Sens. J., vol. 21, no. 16, pp. 17573–17580, 2021.

. M. Harmouch, “Local Binary Pattern Algorithm: The Math Behind It,” The Startup, 2020. https://medium.com/swlh/local-binary-pattern-algorithm-the-math-behind-it-️-edf7b0e1c8b3

. M. Tyagi, “HOG (Histogram of Oriented Gradients): An Overview.” https://towardsdatascience.com/hog-histogram-of-oriented-gradients-67ecd887675f

. B. E. Park, W. S. Jang, and S. K. Yoo, “Texture analysis of supraspinatus ultrasound image for computer aided diagnostic system,” Healthc. Inform. Res., vol. 22, no. 4, pp. 299–304, 2016.

. T. Shin, “A Mathematical Explanation of Support Vector Machines,” Towards Data Science, 2021. https://towardsdatascience.com/a-mathematical-explanation-of-support-vector-machines-e433ffe04362

. I-king-of-ml, “KNN(K-Nearest Neighbour) algorithm, maths behind it and how to find the best value for K,” Medium, 2019. https://medium.com/@rdhawan201455/knn-k-nearest-neighbour-algorithm-maths-behind-it-and-how-to-find-the-best-value-for-k-6ff5b0955e3d#:~:text=In the classification problem%2C the,of all classes of K

. P. B. Padol and S. D. Sawant, "Fusion classification technique used to detect downy and Powdery Mildew grape leaf diseases," 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India, 2016, pp. 298-301, doi: 10.1109/ICGTSPICC.2016.7955315.




How to Cite

Prasad P. S. (2024). Optimized Feature Extraction Model on RCNN and BPNN Models for Grape Disease Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3517 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6062



Research Article