Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection

Title

Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection

Creator

Saha A.; Sharma V.; Mondal R.; Mishra S.; Daramola I.; Abbas A.

Description

It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE.

Source

4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Deep Learning; EfficientNetB3; Mobile Application; Pathology detection; Plant Disease; Regularization; Transfer Learning

Coverage

Saha A., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Mondal R., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Daramola I., University of Bolton, School of Creative Technologies, United Kingdom; Abbas A., University of Bolton, School of Creative Technologies, United Kingdom

Rights

Restricted Access

Relation

ISBN: 979-835030775-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICIPTM59628.2024.10563558

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197703419&doi=10.1109%2fICIPTM59628.2024.10563558&partnerID=40&md5=bef815f71481fca9e09ee6ceea3cc290