We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique.
- Authors
Rashid, Javed; Khan, Imran; Ali, Ghulam; ur Rehman, Shafiq; Alturise, Fahad; Alkhalifah, Tamim
- Abstract
The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments, soil conditions and higher human consumption. It is cultivated in vast areas of Asian and Non-Asian countries, including Pakistan. The guava plant is vulnerable to diseases, specifically the leaves and fruit, which result in massive crop and profitability losses. The existing plant leaf disease detection techniques can detect only one disease from a leaf. However, a single leaf may contain symptoms of multiple diseases. This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps. Firstly, Guava Infected Patches Modified MobileNetV2 and U-Net (GIP-MU-NET) has been proposed to segment the infected guava patches. The proposed model consists of modified MobileNetv2 as an encoder, and the U-Net model's up-sampling layers are used as a decoder part. Secondly, the Guava Leaf SegmentationModel (GLSM) is proposed to segment the healthy and infected leaves. In the final step, the Guava Multiple Leaf Diseases Detection (GMLDD) model based on the YOLOv5 model detects various diseases from a guava leaf. Two self-collected datasets (the Guava Patches Dataset and the Guava Leaf Diseases Dataset) are used for training and validation. The proposed method detected the various defects, including five distinct classes, i.e., anthracnose, insect attack, nutrition deficiency, wilt, and healthy. On 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.
- Subjects
PAKISTAN; GUAVA; DEEP learning; CROP losses; PLANT diseases; FOLIAGE plants; ANTHRACNOSE
- Publication
Computers, Materials & Continua, 2023, Vol 75, Issue 1, p1235
- ISSN
1546-2218
- Publication type
Article
- DOI
10.32604/cmc.2023.032005