Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

Poonam Dhiman, Amandeep Kaur, Yasir Hamid, Eatedal Alabdulkreem, Hela Elmannai and Nedal Ababneh
Additional contact information
Poonam Dhiman: Government PG College, Ambala Cantt 133001, India
Amandeep Kaur: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140601, India
Yasir Hamid: Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates
Eatedal Alabdulkreem: Department of Computer Science, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Hela Elmannai: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Nedal Ababneh: Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates

Sustainability, 2023, vol. 15, issue 5, 1-18

Abstract: In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently difficult. The use of intelligent machines in contemporary horticulture is being hampered by these restrictions, which are emerging as a new barrier. In this research, we present an efficient model for citrus fruit disease prediction. The proposed model utilizes the fusion of deep learning models CNN and LSTM with edge computing. The proposed model employs an enhanced feature-extraction mechanism, with a down-sampling approach, and then a feature-fusion subsystem to ensure significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy. This research utilizes the online Kaggle and plan village dataset which contains 2950 citrus fruit images with disease categories black spots, cankers, scabs, Melanosis, and greening. The proposed model and existing model are tested with two features with pruning and without pruning and compared based on various performance measuring parameters, i.e., precision, recall, f-measure, and support. In the first phase experimental analysis is performed using Magnitude Based Pruning and in the second phase Magnitude Based Pruning with Post Quantization. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, which is better as compared to the existing CNN method.

Keywords: citrus fruit disease; deep learning method; edge computing; pruning feature; CNN; LSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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