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Article Citation - WoS: 6Citation - Scopus: 6Ensemble Transfer Learning Using Maizeset: a Dataset for Weed and Maize Crop Recognition at Different Growth Stages(Elsevier Sci Ltd, 2024) Das, Zeynep Dilan; Alam, Muhammad Shahab; Khan, Muhammad UmerMaize holds significant importance as a staple food source globally. Increasing maize yield requires the effective removal of weeds from maize fields, as they pose a detrimental threat to the growth of maize plants. In recent years, there has been a drive towards Precision Agriculture (PA), involving the integration of farming methods with artificial intelligence and advanced automation techniques. In the realm of PA, deep learning techniques present a promising solution for addressing the complex challenge of classifying maize plants and weeds. In this work, a deep learning method based on transfer learning and ensemble techniques is developed. The proposed method is implementable on any number of existing CNN models irrespective of their architecture and complexity. The developed ensemble model is trained and tested on our custom-built dataset, namely MaizeSet, comprising 3330 images of maize plants and weeds under varying environmental conditions. The performance of the ensemble model is compared against individual pre-trained VGG16 and InceptionV3 models using two experiments: the identification of weeds and maize plants, and the identification of the various vegetative growth stages of maize plants. VGG16 attained an accuracy of 83% in Experiment 1 and 71% in Experiment 2, while InceptionV3 showcased improved performance, boasting an accuracy of 98% in Experiment 1 and 81% in Experiment 2. With the proposed ensemble approach, VGG16 when combined with InceptionV3, achieved an accuracy of 90% for Experiment 1 and 80% for Experiment 2. The findings demonstrate that integrating a suboptimal pre-defined classifier, specifically VGG16, with a more proficient model like InceptionV3, yields enhanced performance across various analytical metrics. This underscores the efficacy of ensemble techniques in the context of maize classification and analogous applications within the agricultural domain.Conference Object Citation - WoS: 7Attitude Control of Quad-Copter Using Deterministic Policy Gradient Algorithms (dpga)(Ieee, 2019) Ghouri, Usama Hamayun; Zafar, Muhammad Usama; Bari, Salman; Khan, Haroon; Khan, Muhammad UmerIn aerial robotics, intelligent control has been a buzz for the past few years. Extensive research efforts can be witnessed to produce control algorithms for stable flight operation of aerial robots using machine learning. Supervised learning has the tendency but training an agent using supervised learning can be a tedious task. Moreover, the data gathering could be expensive and always prone to inaccuracies due to parametric variations and system dynamics. An alternative approach is to ensure the stability of the aerial robots with the help of Deep Re-inforcement Learning (DRL). This paper deals with the intelligent control of quad-copter using deterministic policy gradient algorithms. In this research, state of the art Deep Deterministic Policy Gradient (DDPG) and Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithms are employed for attitude control of quad-copter. An open source simulation environment GymFC is used for training of quad-copter. The results for comparative analysis of DDPG & D4PG algorithms are also presented, highlighting the attitude control performance.Conference Object Citation - WoS: 3Biomechanical Design and Control of Lower Limb Exoskeleton for Sit-to-Stand and Stand-to-Sit Movements(Ieee, 2018) Qureshi, Muhammad Hamza; Masood, Zeeshan; Rehman, Linta; Owais, Muhammad; Khan, Muhammad UmerIn this paper, we present design and development phase of lower limb robotic exoskeleton that can assist paralyzed individuals. Motion of the human wearing exoskeleton is introduced by actuators. Both exoskeleton legs are attached to the supporting frame by passive universal joints. The exoskeleton provides 3 DOFs per limb of which two joints are active and one passive. The control actions i.e., sit-to-stand and stand-to-sit movements are triggered using Double Pole Double Throw (DPDT) toggle switch. The control scheme is implement using Switch control method and the feedback is provided by means of current measurement. This assistive device can be utilized for the disabled persons. The simulation results are provided that evaluates the performance of the control actions on exoskeleton.Conference Object Citation - WoS: 4Sliding Mode Control for Autonomous Flight of Tethered Kite Under Varying Wind Speed Conditions(Ieee, 2020) Bari, Salman; Khan, Muhammad UmerHigh altitude wind is an energy-abundant source, representing the next generation of wind power technology. The power that can be extracted from wind grows cubically with wind speed, making higher altitudes a desirable choice to harvest wind energy. In this respect, large and fully-automated kites or planes can be used to capture such energy. Flight control is a key research area for using fully-automated kite power systems at utility scale. In this study, a novel control architecture is proposed for autonomous pattern 8 flight of tethered kites under varying wind speed conditions. The proposed scheme does not require a separate control system for turn maneuvers and straight flight path sections. Exponential reaching law-based Sliding Mode Control (SMC) and adaptive sliding mode control schemes are tested for flight control of a kite given a pre-specified trajectory. In this approach, the inversion of plant model is not required to address the problem of possible system instability, thus making the scheme proposed here more resilient towards system perturbations.Conference Object Citation - WoS: 84Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture(Ieee, 2020) Alam, Mansoor; Alam, Muhammad Shahab; Roman, Muhammad; Tufail, Muhammad; Khan, Muhammad Umer; Khan, Muhammad TahirTraditional agrochemical spraying techniques often result in over or under-dosing. Over-dosing of spray chemicals is costly and pose a serious threat to the environment, whereas, under-dosing results in inefficient crop protection and thereby low crop yields. Therefore, in order to increase yields per acre and to protect crops from diseases, the exact amount of agrochemicals should be sprayed according to the field/crop requirements. This paper presents a real-time computer vision-based crop/weed detection system for variable-rate agrochemical spraying. Weed/crop detection and classification were performed through the Random Forest classifier. The classification model was first trained offline with our own created dataset and then deployed in the field for testing. Agrochemical spraying was done through application equipment consisting of a PWM-based fluid flow control system capable of spraying the desired amounts of agrochemical directed by the vision-based feedback system. The results obtained from several field tests demonstrate the effectiveness of the proposed vision-based agrochemical spraying framework in real-time.
