Background: Wild animal tracking is crucial for conservation, especially for threatened species like tigers. The Amur tiger is critically endangered, with fewer than 600 left in the wild. The integration of machine learning and drone technology has revolutionized tiger tracking. Drones, or UAVs, play a vital role in wildlife conservation, while machine learning algorithms analyze complex data, make predictions and automate decision-making. This combination enables efficient processing of drone-generated data, including images, sounds and sensor readings. 
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Methods: This study utilized a dataset comprising 4,413 images sourced from Kaggle, originally derived from the ATRW (Annotated Tigers in the Wild) dataset. To ensure compatibility with the YOLOv8 model, the images were reformatted from PASCAL VOC annotation format to YOLO Darknet format. A series of preprocessing techniques were implemented, including image resizing, data augmentation (such as rotation, horizontal flipping and brightness adjustment) and pixel value normalization to enhance model generalization. The dataset was systematically partitioned into training, validation and test subsets to facilitate robust model evaluation and performance assessment. 
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Result: The YOLOv8 model exhibited high efficacy in detecting tigers across diverse environmental conditions. It achieved a final Mean Average Precision (mAP) of 0.944, indicating robust detection accuracy. The model demonstrated strong generalization capabilities, effectively identifying tigers under varying illumination, occlusion and background complexities. These findings highlight the potential of YOLOv8 as a reliable tool for automated wildlife monitoring and conservation efforts. 
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Wild Animal Tracking for Effective Wildlife Conservation using YOLOv8 and Machine Learning Technologies

Background: Soybean, a crucial oilseed crop in India, is susceptible to various diseases, including Yellow Mosaic Disease caused by Mungbean Yellow Mosaic Virus (MYMV). Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crop. Therefore, this study aims to evaluate the efficacy of YOLOv8 for detecting Mungbean Yellow Mosaic Virus in soybean crops. 
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Methods: Images were captured using 48 MP mobile camera from the experimental fields of Main Agricultural Research Station, University of Agricultural Sciences, Dharwad during 2024 summer season. The captured images were annotated, augmented using various transformations, resized and normalized. As a result, a dataset of 4480 annotated images of MYMV-infected soybean plants was created. The dataset was randomly split into training and validation set. Different variants of YOLOv8 models were trained and evaluated based on precision, recall, mean Average Precision (mAP) and inference time. 
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Result: Among the variants, YOLOv8x demonstrated superior performance, with a precision of 98.1%. Whereas the recall (78.6%), mAP@0.5 (85.3%) and F1-score (86.9%) were higher in YOLOv8s. YOLOv8n boasted the lowest inference time of 3.6 milliseconds and the least number of model parameters (3.1 millions). YOLOv8s shows promise as an efficient and accurate tool for MYMV detection in soybean crop. Its low inference time and high precision make it suitable for real-world deployment, aiding in early disease detection and precise management of the disease. This study contributes to advancing disease detection techniques in agriculture, facilitating early intervention and minimizing crop losses for sustainable agricultural practices.

Artificial Intelligence based Precise Disease Detection in Soybean using Real Time Object Detectors

Background: Tigers, as iconic apex predators and symbols of biodiversity conservation, face numerous threats to their existence. Effective tracking and monitoring are essential for understanding and preserving these majestic creatures and their habitats. The convergence of machine learning and drone technology has emerged as transformative tools in the field of tiger tracking. Drones, or Unmanned Aerial Vehicles (UAVs), have rapidly become invaluable assets in wildlife conservation. Machine learning algorithms, with their capacity to analyze complex datasets, make predictions and automate decision-making processes, offer a novel approach to processing the massive amounts of data generated by drones, including images, sounds and sensor readings. 
 
Methods: This paper explores the historical significance of tiger tracking, the pivotal role of drones in conservation and the transformative capabilities of machine learning in wildlife monitoring. In this work, an accurate framework for tiger detection based on YOLOv8 is utilized. 
 
Result: By examining the interplay between machine learning, drone technology and tiger conservation, this paper highlights the potential for innovation and the challenges that lie ahead, promising a brighter future for these iconic creatures and their ecosystems. The fine-tuned YOLOv8 model demonstrates exceptional object detection performance, boasting a mAP50 of 0.9820 and a mAP50-95 of 0.6856, coupled with precise classification (precision 0.9646) and robust instance capture (recall 0.9580).

Wild Animal Tracking for Effective Wildlife Conservation using YOLOv8 and Machine Learning Technologies

Artificial Intelligence based Precise Disease Detection in Soybean using Real Time Object Detectors

Application of Machine Learning in Drone Technology for Tracking of Tigers