Hybrid Deep Learning-Based Potato andTomato Leaf Disease Classification

Title

Hybrid Deep Learning-Based Potato andTomato Leaf Disease Classification

Creator

Patil M.A.; Manohar M.; Laxuman C.; Parane K.; Dodamani B.M.; Sunkad G.

Description

Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-891, pp. 157-174.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Crop disease; Hybrid model; MobileNet; OCNN-OLSTM; ResNet50

Coverage

Patil M.A., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manohar M., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India; Laxuman C., Zonal Agricultural Research Station, UAS-R, Karnataka, Kalaburagi, 585101, India; Parane K., NITK Surathkal, Karnataka, Surathkal, India; Dodamani B.M., Zonal Agricultural Research Station, UAS-R, Karnataka, Kalaburagi, 585101, India; Sunkad G., University of Agricultural Sciences, Karnataka, Raichur, 584104, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981999523-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-9524-0_13

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187647518&doi=10.1007%2f978-981-99-9524-0_13&partnerID=40&md5=b36ccd6a30bf9b01413f71cd4ad5723a