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Real-Time Multiple guava leaf Disease Detection from a Single leaf Using Hybrid Deep Learning Technique

Computers, Materials & Continua 2023年第1期74卷 1235-1257页

作者： Javed Rashid Imran Khan Ghulam Ali Shafiq ur Rehman Fahad Alturise Tamim Alkhalifah Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Department of CS University of OkaraOkara56310Pakistan Department of Botany University of OkaraOkara56310Pakistan Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassQassim52571Saudi Arabia

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human *** is cultivated in vast areas of Asian and Non-Asian countries,including *** guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability *** existing plant leaf disease detection techniques can detect only one disease from a ***,a single leaf may contain symptoms of multiple *** study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several ***,guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava *** proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder ***,the guava leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected *** the final step,the guava Multiple leaf diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava *** self-collected datasets(the guava Patches dataset and the guava leaf diseases dataset)are used for training and *** proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and *** average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

关键词： guava leaf diseases guava leaf segmentation guava patches segmentation multiple leaf diseases guava leaf diseases dataset

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