EAI Endorsed Transactions on AI and Robotics

Apple Disease Detection and Classification using Random Forest (One-vs-All)

2024-12-04T01:41:51+00:00

Fruit diseases detection and recognition are a common problem worldwide. Fruit Disease detection is a hot topic among researchers and is very complex due to structure and color similarity factors. In this research we proposed a new model to detect and classify the apple disease with the help of digital image processing and machine learning. First, image processing techniques were applied to enhance the image contrast and remove the noise, which helped to segment the region of interest accurately also help to extract feature without garbage data. Then K-means clustering technique with fuzzy C-mean method was implemented to segment the images. GLCM feature extraction was used after the segmentation. Real images of apple disease with multi-disease regions were used in the research method. These features were preprocessed with methods like LDA. K-Fold cross validation was used for training and testing, with combination of random forest machine learning method. The result showed high accuracy with comparison of existing techniques.

Advancing Food Security through Precision Agriculture: YOLOv8’s Role in Efficient Pest Detection and Management

2024-12-04T09:56:28+00:00

In response to the growing global population and the consequent need for sustainable food security, effective pest management is critical for enhancing agricultural productivity. This research presents YOLOv8, a state-of-the-art deep learning model optimized for pest detection in agricultural environments, contributing to modern food security efforts. Evaluated using the complex IP102 dataset, YOLOv8 demonstrated notable improvements in pest detection accuracy, achieving scores of 66.9 mAP@0.5 and 42.1 mAP@[0.5:0.95]. These results underscore YOLOv8’s robust performance across diverse detection scenarios, enabling more precise pest control and reducing crop loss. However, in-depth dataset analysis revealed a bias towards larger pests, likely due to bounding box size variations, which presents an opportunity for model improvement. Future work will focus on addressing data imbalances, enhancing sensitivity to smaller pests, and validating YOLOv8 in varied real-world agricultural settings. These advancements are expected to significantly improve pest management practices, ultimately boosting agricultural productivity and supporting global food security through the application of modern agricultural technologies.

Enhancing Virtual Reality Experiences in Architectural Visualization of an Academic Environment

2024-12-04T10:12:22+00:00

Virtual Reality (VR) technology possesses the capability to transport users into immersive, alternative environments, providing them with a convincing sense of presence within a simulated world. This project leverages VR to develop an interactive, educational system centered around the De Montfort University (DMU) Queens Building, simulating key facilities and infrastructure through the integration of 360-degree imagery and Adobe Captivate software. Designed in response to contemporary challenges, such as the COVID-19 pandemic, which underscored the need for flexible and innovative learning methodologies, the VR system offers an immersive educational platform enriched with essential information to enhance student engagement and learning outcomes. A comprehensive literature review explored the expanding applications of VR across diverse sectors, including education, healthcare, robotics, and manufacturing. The findings of this review underscored VR's transformative potential in enhancing educational engagement and facilitating a deeper understanding of complex concepts. The project methodology involved meticulously mapping the physical layout of the Queens Building, capturing targeted 360-degree scenarios using a Ricoh Theta V camera, and subsequently transforming these into immersive VR scenarios enriched with interactive hotspots, meticulously synchronized with the building's design layout. The VR system successfully achieved the project's objectives by simulating key educational and informational use cases. It provides students with an alternative learning medium, offering interactive insights into the functionalities of equipment and facilities within the building. Furthermore, the system enables a virtual tour of the DMU campus, facilitating familiarization with the university environment. Findings from the VR application highlight its potential as a dynamic educational tool, positioning it as a valuable complement to traditional learning methods. This innovative approach demonstrates the capacity of VR to enhance student understanding, support academic and research pursuits, and ultimately enrich the overall student experience.