Browsing by Author "Alam, Muhammad Shahab"
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Article Citation Count: 0Ensemble transfer learning using MaizeSet: A dataset for weed and maize crop recognition at different growth stages(Elsevier Sci Ltd, 2024) Khan, Muhammad Umer; Alam, Muhammad Shahab; Khan, Muhammad Umer; Mechatronics EngineeringMaize 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 Count: 57Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture(Ieee, 2020) Khan, Muhammad Umer; Alam, Muhammad Shahab; Roman, Muhammad; Tufail, Muhammad; Khan, Muhammad Umer; Khan, Muhammad Tahir; Mechatronics EngineeringTraditional 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.Article Strawberries Maturity Level Detection Using Convolutional Neural Network (CNN) and Ensemble Method(Computer Vision and Machine Learning in Agriculture, 2023) Khan, Muhammad Umer; Khan, Muhammad Umer; İrfanoğlu, Bülent; Alam, Muhammad Shahab; Mechatronics Engineering; Department of Mechatronics EngineeringHarvesting high-quality products at an affordable expense has been the prime incentive for the agriculture industry. Automation and intelligent software technology is playing a pivotal role in achieving both practical and effective solutions. In this study, we developed a robust deep learning-based vision framework to detect and classify strawberries according to their maturity levels. Due to the unavailability of the relevant dataset, we built up a novel dataset comprising 900 strawberry images to evaluate the performance of existing convolutional neural network (CNN) models under complex background conditions. The overall dataset is categorized into three classes: mature, semi-mature, and immature. The existing classifiers evaluated during this study are AlexNet, GoogleNet, SqueezeNet, DenseNet, and VGG-16. To further improve the overall prediction accuracy, two Ensemble methods are proposed based on SqueezeNet, GoogleNet, and VGG-16. Based on the considered performance matrices, SqueezeNet is recommended as the most effective model among all the classifiers and networks for detecting and classifying the maturity levels of strawberries.Article Citation Count: 10TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots(Mdpi, 2022) Khan, Muhammad Umer; Alam, Mansoor; Güneş, Ahmet; Khan, Muhammad Umer; Gunes, Ahmet; Salah, Bashir; Khan, Muhammad Tahir; Mechatronics Engineering; Department of Mechatronics EngineeringSelective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants.