Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution
subject: Decision Tree; Deep Learning; Disease Magnitude; Multi-classification; Wheat Diseases; Yellow Rust
description: Wheat rust disease poses a significant danger to global food security and requires rapid, precise diagnosis to be effectively managed. Using a hybrid deep learning (DL) model consisting of a convolutional neural network (CNN) and a decision tree (DT), a new method for classifying wheat rust illness across six magnitude scales has been described in the proposed study. For training and assessing the model, a dataset of 50,000 wheat leaf photos representing a wide range of disease magnitude has been amazing. The suggested work developed a hybrid CNN-DT model with an amazing overall accuracy of 93.47% by carefully analyzing the data and crafting the model. The model's resilience in identifying multiple levels of disease magnitude was proved by the performance metrics for each disease magnitude class. The proposed hybrid model also outperformed state-of-the-art models in terms of accuracy, as shown by the comparisons conducted. The findings provide important new information on the potential of DL methods for wheat rust disease classification, which can then be used as a trusted resource for early disease diagnosis and smarter agricultural policymaking. In the face of agricultural diseases, the suggested model has important implications for improving crop management, reducing yield losses, and guaranteeing food security. 2023 IEEE.
creator: Konidena A.; Shanbhog M.; Singh S.; Sharma V.; Jain A.K.; Sharma N.
source: Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 648-654.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034233-8
format: Online
language: English
type: Conference paper
coverage: Konidena A., Iilm University, Department of Cse, Uttar Pradesh, Greater Noida, India; Shanbhog M., Christ University, Department of Computer Science, Uttar Pradesh, Ghaziabad, India; Singh S., School of Computing MIT-ADTU, Department of Information Technology, Pune, India; Sharma V., Graphic Era Hill University, Uttarakhand, Dehradun, India; Jain A.K., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sharma N., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India