Usama Athar

Analyzing UAV-Based Multispectral Data for Genotypic-Aware Crop Performance Assessment

Muhammad Tayyab Iftikhar, Abdullah Imran, Muhammad Hissan Umar, Usama Athar et autres

High-throughput and accurate trait characterization is critical for accelerating crop improvement, yet conventional field methods often fail to capture genotype-specific, stage-level temporal dynamics relevant to yield. In this study, UAV-based multispectral imagery across $\mathbf{1 6}$ growth-stage timestamps revealed key phenological and physiological …

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Benchmarking control strategies for UAV swarms: Centralized, decentralized, or federated reinforcement learning

Mirza Aqib Ali, Adnan M. Maqsood, Usama Athar, Sara Ali

Autonomous UAV swarms are becoming increasingly important in mission-critical domains where coordination depends on training approaches that enable effective cooperation among agents. This work presents an evaluation of three dominant reinforcement learning paradigms, centralized, decentralized, and federated, within the context of cooperative …

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Pixel-Based Glacial Change Monitoring Across Hindu Kush, Karakoram, & Himalayan Ranges

Muhammad Sameer Amjad, Muhammad Jamshaid Ghaffar, Usama Athar, Muhammad Moazam Fraz

Glaciers in the high-mountain regions of the Hindu Kush, Karakoram, and Himalayas are critical for sustaining downstream water resources, yet their short-term and localized responses to climate variability remain unclear. To address this gap, we present a machine learning–based framework for high-resolution …

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Semi-Supervised Contrastive Representation Learning for Sunflower Phenology Estimation

Saleha Zainab Fatima, Munazza Raees, Usama Athar, M. Bilal Asif et autres

Monitoring the phenological stages of crops is essential for yield forecasting and informed agronomic interventions, yet it remains constrained due to reliance on manual observations or simple vegetation indices, which often vary with illumination and atmospheric conditions. This study presents a semi-supervised …

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Phenology-aware in-season crop yield estimation through UAV multispectral imagery and deep neural networks

Usama Athar, Muhammad Ali, Zuhair Zafar, Zahid Mahmood et autres

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Large Language Models Meet Molecules: A Systematic Review of Advances and Challenges in AI-Driven Cheminformatics

Muhammad Saad Umer, Muhammad Nabeel, Usama Athar, Iseult Lynch et autres

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Combining UAV Multispectral Data & Crop Morphological Features for Sunflower Oil Content Estimation

Muhammad Ali, Usama Athar, Zuhair Zafar, Hasan Ali Khattak et autres

Accurate pre-harvest estimation of seed composition is imperative for advancing field management practices and refining plant phenotyping approaches. This research pioneers the utilization of artificial intelligence (AI) methods built upon crop morphological and unmanned aerial vehicle (UAV) multispectral data to estimate sunflower …

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Comparative Evaluation of Reinforcement Learning Algorithms for Multi-Agent Unmanned Aerial Vehicle Path Planning in 2D and 3D Environments

Adnan M. Maqsood, Usama Athar, Hasan Raza Khanzada

Path planning in multi-agent UAV swarms is a crucial issue that involves avoiding collisions in dynamic, obstacle-filled environments while consuming the least amount of time and energy possible. This work comprehensively evaluates reinforcement learning (RL) algorithms for multi-agent UAV path planning in …

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Sunflower Lodging Detection and Monitoring Through UAV-Based Multispectral Data

Usama Athar, Muhammad Umair Ali, Zuhair Zafar, Haris Khurshid et autres

Sunflower lodging significantly hampers agricultural productivity, resulting in notable yield reductions and increased harvesting challenges. This study investigates the effectiveness of UAV-based multispectral imagery in monitoring sunflower lodging across various sunflower varieties. High-resolution multispectral data is collected from 117 sunflower plots in …

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Merging UAV-Derived Metrics with Crop Physiology to Estimate Sunflower Yield

Usama Athar, Muhammad Ali, Zuhair Zafar, Haris Khurshid et autres

Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need for precise sunflower seed yield predictions. This study proposes a combined …

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Analyzing Phenological Progression in Wheat Genotypes Through UAV Multispectral Imagery

Usama Athar, Muhammad Ali, Zuhair Zafar, Karsten Berns et autres

Efficient crop management necessitates synchronizing irrigation, fertilization, and pest control with specific growth stages to optimize resource use and reduce losses. Monitoring wheat phenology is crucial for improving crop management and increasing yield. This study employs UAV multispectral data, enhanced with AI …

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Analyzing Fish Wellness Using Spatiotemporal Data & Behavior Recognition

Kanwal Aftab, Mammona Qudsia, Usama Athar, Muhammad Moazam Fraz

Monitoring fish behavior is essential for fisheries research and aquaculture, as it plays a significant role in assessing fish well-being and applying effective husbandry practices. Although previous research has focused on fish tracking, there remains a need for post-analysis techniques that offer …

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