A Hybrid Deep Learning Approach to Classify the Plant Leaf Species  

作　　者：Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas 

机构地区：[1]Department of CS&SE,Islamic International University,Islamabad,44000,Pakistan [2]Department of IT Services,University of Okara,Okara,56310,Pakistan [3]Faculty of Science and Arts Belqarn,University of Bisha,Sabtul Alaya,61985,Saudi Arabia [4]Department of CS,University of Okara,Okara,56310,Pakistan [5]Department of Computer Science&Engineering,University of Engineering&Technology Lahore,Narowal,Campus,Narowal,51601,Pakistan [6]Electrical Engineering Department,College of Engineering,King Khalid University,Abha,61421,Saudi Arabia [7]Research Center for Advanced Materials Sciences(RCAMS),King Khalid University,Abha,Saudi Arabia

出　　处：《Computers, Materials & Continua》2023年第9期3897-3920,共24页计算机、材料和连续体（英文）

基　　金：funding this work through the Research Group Program under the Grant Number:(R.G.P.2/382/44).

摘　　要：Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关 键 词：Plant leaf species stacking ensemble model GUAVA POTATO java plum MobileNetV2-UNET hybrid deep learning segmentation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]