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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Janrao, Surekha
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Computer Science & IT Engineering

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Efficient model for cotton plant health monitoring via YOLO-based disease prediction Pavate, Aruna; Kukreja, Swetta; Janrao, Surekha; Bankar, Sandip; Patil, Rohini; Bidve, Vijaykumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp164-178

Abstract

Protecting plants from diseases involves recognizing the symptoms and identifying practical, safe, and reasonable treatment methods. Holistic approaches based on particular times or seasons can reduce plant resistance and minimize tedious work. Technological advancements have led to the development of microscopic examinations and computational methods using machine learning techniques to detect diseases automatically and quickly using leaf images. This study builds the prediction model using EfficientNet and YOLO neural network architectures from computer vision. The development of a model that assists farmers in identifying cotton disease so that they use pesticides that may treat it further utilizes this concept. In the physical world, the input is accepted from many different sources, so observing the model’s output is necessary. This work concentrates on model response to the inputs from physical devices, and analysis shows that the monitoring varies the results. A novel convolutional neural network (CNN) based on the EfficientNet architectures and variations of YOLO architectures is used to classify and identify the objects in cotton leaf. The EfficientNetB4 yielded 100% accuracy for healthy leaf and powdery mild leaf classes, and YOLO v4 version with 96%, 98.3%, 99.2%, and 0.70 for precision, recall, mAP@0.5, mAP120.5:095 respectively. These results indicate that consequences vary in real-time per environmental parameters such as light effect and devices, and analysis shows that monitoring affects the results.
Deep learning approaches for Braille detection and classification: comparative analysis Janrao, Surekha; Fernandes, Tavion; Golatkar, Ojas; Dusane, Swaraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4652-4660

Abstract

This study proposes a hybrid approach to Braille translation leveraging the strengths of both YOLO for object detection and multitude of classification models such as ResNet, and ResNet for accurate Braille character classification from images. Upon comparing numerous models on various performance metrics, ResNet and DenseNet outperformed other models, exhibiting high accuracy (0.9487 and 0.9647 respectively) and F1-scores (0.9481 and 0.9666) due to their deep, densely connected architectures adept at capturing intricate Braille patterns. CNNs with pooling showed balanced results, while MobileNetV2's lightweight design limited complex classification. ResNeXt's multi-path learning achieved respectable performance but lagged behind ResNet and DenseNet. In the future the results from our study could be further explored on contracted Braille recognition, be adapted to various Braille codes, and optimized for mobile devices, for real time Braille detection and translation on smartphones.
Co-Authors Bankar, Sandip Bidve, Vijaykumar Dusane, Swaraj Fernandes, Tavion Golatkar, Ojas Kukreja, Swetta Patil, Rohini Pavate, Aruna