Deep learning for medium-scale agricultural crop detection through aerial view images

This research project focuses on utilizing two state-of-the-art YOLOv4-based deep learning models, for large-scale agricultural crop detection using Unmanned Aerial Vehicles (UAVs). The objective is to develop an accurate and efficient crop detection system capable of identifying chili crops, eggplant crops, and empty polybags in agricultural fields. Crops detection is important for the development of a robotic vision in maximize the productivity and efficiency in agriculture associate with the development of concept Industry 4.0. This study seek to explore the comparison between YOLOv4 and YOLOv4 tiny model in term of mean average precision (mAP), precision, recall, F1-score, detection time and memory consumption. A custom dataset with 300 images was collected and annotated into total bounding boxes of 23335 with 6969 chili tree, 15402 eggplant tree and 964 empty polybag. The dataset was separated into train, validation and test set with the ratio of 70:20:10. The dataset was trained into YOLOv4 and YOLOv4 tiny with 2000 iterations. The result has shown that the YOLOv4 has the higher mean AP of 91.49% with 244.2mb memory storage consumption while YOLOv4 tiny achieve lower mean AP of 71.83% with 22.4mb. In summary, this research has significated the implementation of deep learning models to perform large-scale agricultural crop detection and can be further develop into automation industrial 4.0 of local agricultural sector

Chili plant classification using transfer learning models through object detection

This study presents an application of using a Convolutional Neural Network (CNN) based detector to detect chili and its leaves in the chili plant image. Detecting chili on its plant is essential for the development of robotic vision and monitoring. Thus, helps us supervise the plant growth, furthermore, analyses their productivity and quality. This paper aims to develop a system that can monitor and identify bird’s eye chili plants by implementing machine learning. First, the development of methodology for efficient detection of bird’s eye chili and its leaf was made. A dataset of a total of 1866 images after augmentation of bird’s eye chili and its leaf was used in this experiment. YOLO Darknet was implemented to train the dataset. After a series of experiments were conducted, the model is compared with other transfer learning models like YOLO Tiny, Faster R-CNN, and EfficientDet. The classification performance of these transfer learning models has been calculated and compared with each other. The experimental result shows that the Yolov4 Darknet model achieves mAP of 75.69%, followed by EfficientDet at 71.85% for augmented dataset

Features extraction of capsicum frutescens (C.F) NDVI values using image processing

There is yet an application for monitoring plant condition using the Normalized Difference Vegetation Index (NDVI) method for Capsicum Frutescens (C.F) or chili. This study was carried out in three phases, where the first and second phases are to create NDVI images and recognize and extract features from NDVI images. The last stage is to assess the efficiency of Neural Network (N.N.), Naïve Bayes (N.B.), and Logistic Regression (L.R.) models on the classification of chili plant health. The images of the chili plant will be captured using two types of cameras, which can be differentiated by whether or not they have an infrared filter. The images were collected to create datasets, and the NDVI images' features were extracted. The 120 NDVI images of the chili plant were divided into training and test datasets, with 70.0% training and 30.0% test. The extracted data was used to test the classification accuracy of classifiers on datasets. Finally, the N.N. model was found to have the highest classification accuracy, with 96.4 % on the training dataset and 88.9 % on the test dataset. The state of the chili plant can be predicted based on feature extraction from NDVI images by the end of the study

The diagnosis of diabetic retinopathy by means of transfer learning and fine-tuned dense layer pipeline

Diabetes is a global disease that occurs when the body is disabled pancreas to secrete insulin to convert the sugar to power in the blood. As a result, some tiny blood vessels on the part of the body, such as the eyes, are affected by high sugar and cause blocking blood flow in the vessels, which is called diabetic retinopathy. This disease may lead to permanent blindness due to the growth of new vessels in the back of the retina causing it to detach from the eyes. In 2016, 387 million people were diagnosed with Diabetic retinopathy, and the number is growing yearly, and the old detection approach becomes worse. Therefore, the purpose of this paper is to computerize the old method of detecting different classes of DR from 0-4 according to severity by given fundus images. The method is to construct a fine-tuned deep learning model based on transfer learning with dense layers. The used models here are InceptionV3, VGG16, and ResNet50 with a sharpening filter. Subsequently, InceptionV3 has achieved 94% as the highest accuracy among other models

The classification of heart murmurs: The identification of significant time domain features

Phonocardiogram (PCG) is a type of acoustic signal collected from the heartbeat sound. PCG signals collected in the form of wave files and collected type of heart sound with a specific period. The difficulty of the binomial class in supervised machine learning is the steps-by-steps workflow. The consideration and decision make for every part are importantly stated so that misclassification will not occur. For the heart murmurs classification, data extraction has highly cared for it as we might have fault data consisting of outside signals. Before classifying murmurs in binomial, it will involve multiple features for selection that can have a better classification of the heart murmurs. Nevertheless, since classification performance is vital to conclude the results, models are needed to compare the research's output. The main objective of this study is to classify the signal of the murmur via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features by using different feature selection methods. It continues with the classification with different models to compete for the highest accuracy as the performance for murmur classification. A set of Michigan Heart Sound and Murmur database (MHSDB) was provided by the University of Michigan Health System with chosen signals listening with the bell of the stethoscope at the apex area by left decubitus posture of the subjects. The PCG signals are pre-processed by segmentation of three seconds, downsampling eight thousand Hz and normalized to -1, +1. Features are extracted into ten features: Root Mean Square, Variance, Standard Deviation, Maximum, Minimum, Median, Skewness, Shape Factor, Kurtosis, and Mean. Two cross-validation methods applied, such as data splitting and k-fold cross-validation, were used to measure this study's data. Chi-Square and ANOVA technique practice to identify the significant features to improve the classification performance. The classification learners are enrolled and compared by Ada Boost, Random Forest (RF) and Support Vector Machine (SVM). The datasets will separate into a ratio of 70:30 and 80:20 for training and testing data, respectively. The chi-Square selection method was the best features selection method and 80:20 data splitting with better performance than the 70:30 ratio. The best classification accuracy for the models significantly come by SVM with all the categories with 100% except 70:30 test on test data with 97.2% only

Articulated robot arm

In medical rehabilitation programs, trajectory tracking is used to increase the repeatability of joint movement and the patient's recovery in the early phases of rehabilitation. In order to achieve that, the robotic arm has been implemented since it can provide a precise and move in almost perfect motion. This manuscript aim to develop and simulate a 2DOF robotic arn that will able to tracking the trajectory successfully. Hence, in order to achieved that a modeling, simulation, and control of a Two Degree of Freedom (2-DOF) Robot Arm is being discussed in this manuscript. First, the robot specifications, as well as Robot Kinematics forward and inverse kinematics of a 2-DOF robot arm, are provided. The dynamics of the 2-DOF robot arm were then formulated in order to obtain motion equations by using the Eular-Lagrange Equation. For the controller of the robot, a control design was created utilising a PID controller. All the data is recorded from the margin of error as well as the overshoot and peak settling time is being record via matlab. The data is differentiate by with with controller, with PI and PID, in which the error is less than 12.5 and 1.63 consecutively. The data that being gathered show that a controller best suited in this rehabilitation robo

The identification of RFID signal using k-means for pallet-level tagging

Radio Frequency Identification (RFID) applications are becoming increasingly popular in a myriad of areas, and therefore, an effective RFID technology-based location would offer a much-needed additional in tracking system. This research focuses on the identification of the location of passive RFID at the pallet-level, which uses the RFID signal strength to cluster the pallet level tagging through k-means. A comparison between the actual and the predicted level attained via the k-means clustering is evaluated through a multi-class performance metrics. It was demonstrated from the investigation that the k-means model is capable of achieving a classification accuracy of 69% and 67% for the train and test data, respectively

Screw absence classification on aluminum plate via features based transfer learning models

Screw is one of the important elements in every industry. Present of screw play an important role in which it holds the product in its own position and prevent loosen or collision with the case which will cause the small components or compartment fall off from its original position and lead to product failure. With the rise of revolution 4.0 in the industry, it helps to reduce the labor cost and human error. The main purpose of this study is to create a robust classification model used for machine vision detection – absence and present of screw, which could be adapted into respective robotics application system. 6 degree of freedom UR robot, Universal Robot is used to collect the custom dataset in TT Vision Technologies Sdn Bhd. The collected dataset is then classified into two categories, named as absent and present. Pretrained dataset, ImageNet is used to ease the training process in this research. Transfer learning model is used to extract the features which used to feed into different machine learning models. Each machine learning models undergoes hyperparameters tunning to achieve best classification accuracy. Samling ratio of 60:20:20 is used to separate the data in training, validation and testing respectively before fed into different ml model

Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City

A door-to-door journey in a public transportation system is a notable concept that is practically being promoted among users to consider public transport as an important alternative. The door-to-door journey will integrate the travel segments starting from home to destination, including all visible amenities. Users’ preferences on the time travel of these key segments are necessary to be understood. In this case, Machine Learning technique has been seen as a robust computational advancement to forecast their travel mode choice. However, the most convenient model as the best predictor is still questionable. To address this issue, we employed some pre-eminent machine learning models, specifically Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), k-Nearest Neighbor (kNN) as well as Support Vector Machine (SVM), to compare their travel mode choice prediction performance of users in the city of Kuantan. The data collection was conducted in Kuantan City via Revealed/Stated Preferences (RPSP) Survey between 8:00 AM to 5:00 PM on weekdays. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The results depicted that the Random Forest could provide satisfactory classification accuracies for both training and testing data up to 68.3% and 61.3%, respectively, compared to the other evaluated machine learning models. In summary, Random Forest provides a good result in the training and testing data and is considered as the best predictor in this research to forecast users’ mode choice in the city of Kuantan

Sign language recognition using deep learning through LSTM and CNN

This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus eliminating the differences between both parties. The objectives of this thesis are to extract features from the dataset for sign language recognition model and the formulation of deep learning models and the classification performance to carry out the sign language recognition. First, we develop methodology for an efficient recognition of sign language. Next is to develop multiple system using three different model which is LSTM, CNN and YOLOv5 and compare the real time test result to choose the best model with the highest accuracy. We used same datasets for all algorithms to determine the best algorithm. The YOLOv5 has achieved the highest accuracy of 97% followed by LSTM and CNN with 94% and 66.67%