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Article Citation - WoS: 2Citation - Scopus: 1Autonomous Landing of a Quadrotor on a Moving Platform Using Motion Capture System(Springer, 2024) Qassab, Ayman; Khan, Muhammad Umer; Irfanoglu, BulentThis paper investigates the challenging problem of the autonomous landing of a quadrotor on a moving platform in a non-cooperative environment. The limited sensing ability of quadrotors often hampers their utilization for autonomous landing, especially in GPS-denied areas. The performance of motion capture systems (MCSs) in many application areas is the motivation to utilize them for the autonomous take-off and landing of the quadrotor in this research. An autonomous closed-loop vision-based navigation, tracking, and control system is proposed for quadrotors to perform landing based upon Model Predictive Control (MPC) by utilizing multi-objective functions. The entire process is posed as a constrained tracking problem to minimize energy consumption and ensure smooth maneuvers. The proposed approach is fully autonomous from take-off to landing; whereas, the movements of the landing platform are pre-defined but still unknown to the quadrotor. The landing performance of the quadrotor is tested and evaluated for three different movement patterns: static, square-shaped, and circular-shaped. Through experimental results, the pose error between the quadrotor and the platform is measured and found to be less than 30 cm. Introducing a holistic vision system for quadrotor navigation, tracking, and landing on stationary/moving platforms. Proposing an energy-efficient, smooth, and stable MPC controller validated by Lyapunov analysis. Validating the adept tracking and safe landings of the quadrotor on stationary/moving platforms through three diverse experiments.Article Performance Investigation of ML Algorithms for Potato Blight Classification: The Role of Hyperparameter Tuning(Springer, 2026) Saeed, Sadia; Rehman, Hafiz Zia Ur; Hussain, Muhammad Ureed; Khan, Muhammad Umer; Saeed, Muhammad TallalPotato is the world's fourth most important food crop, consumed by over a billion people. Early and late blight diseases can reduce yields by up to 40%, leading to severe economic and food security challenges. While manual detection methods are prone to error, automated, image-based machine learning (ML) offers a promising alternative, though its performance depends strongly on proper optimization. This study investigates the role of hyperparameter tuning in improving ML algorithms for potato blight classification. We utilized two datasets: the PlantVillage dataset (500 images per class) and a region-specific Potato Leaf Dataset (PLD) from Pakistan (1628 early blight, 1424 late blight, 1020 healthy). All images were resized to 256 & times; 256 pixels and augmented. Features were extracted using the Bag-of-Features (BoF) technique, and four classic ML models-Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)-were trained. Hyperparameters were optimized via grid search with 5-fold cross-validation. This tuning led to measurable improvements; for instance, SVM accuracy increased from 93.0% to 95.9% on PlantVillage and from 85.0% to 87.0% on PLD. Evaluation using precision, recall, F1-score, and specificity confirmed SVM as the best-performing model. A confusion matrix analysis revealed that most misclassifications occurred between the two blight types due to visual similarity. To translate our findings into practice, we developed a MATLAB Graphical User Interface (GUI) that enables farmers to classify a leaf image in under three seconds and receive precautionary recommendations. This study demonstrates that systematic hyperparameter optimization is crucial for maximizing ML performance and is a key step in building accessible, real-time tools for precision agriculture. Future work will focus on extending the system to mobile and web platforms.
