The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.

Protected multimodal emotion recognition

In this thesis, we propose Protected Multimodal Emotion recognition (PMM-ER), an emotion recognition approach that includes security features against the growing rate of cyber-attacks on various databases, including emotion databases. The analysis on the frequently used encryption algorithms has led to the modified encryption algorithm proposed in this work. The system is able to recognize 7 different emotions, i.e. happiness, sadness, surprise, fear, disgust and anger, as well as a neutral emotion state, based on 2D video frames, 3D vertices, and audio wave information. Several well-known features are employed, including the HSV colour feature, iterative closest point (ICP) and Mel-frequency cepstral coefficients (MFCCs). We also propose a novel approach to feature fusion including both decision- and feature-level fusion, and some well-known classification and feature extraction algorithms such as principle component analysis (PCA), linear discernment analysis (LDA) and canonical correlation analysis (CCA) are compared in this study.

Protected multimodal emotion recognition

In this thesis, we propose Protected Multimodal Emotion recognition (PMM-ER), an emotion recognition approach that includes security features against the growing rate of cyber-attacks on various databases, including emotion databases. The analysis on the frequently used encryption algorithms has led to the modified encryption algorithm proposed in this work. The system is able to recognize 7 different emotions, i.e. happiness, sadness, surprise, fear, disgust and anger, as well as a neutral emotion state, based on 2D video frames, 3D vertices, and audio wave information. Several well-known features are employed, including the HSV colour feature, iterative closest point (ICP) and Mel-frequency cepstral coefficients (MFCCs). We also propose a novel approach to feature fusion including both decision- and feature-level fusion, and some well-known classification and feature extraction algorithms such as principle component analysis (PCA), linear discernment analysis (LDA) and canonical correlation analysis (CCA) are compared in this study.

Measurement of Leaf Spot Disease Severity in Turmeric Plant

Digital Image Processing (DIP) applications in agriculture sector is becoming popular because of its fast, cost-effective and accurate solutions related to diseases and marketing. The hands-on solutions are being provided through various applications. Leaf Sopot disease has become the major constraint in the turmeric cultivation in India. Colletotrichum capsici is a fungal disease commonly known as leaf Spot. The brown spots of 4-5 cm length and 2-3 cm width with a grey centre are found on either surface of the leaves. If not treated timely, it causes the heavy loss in terms of quality and quantity. This paper proposes the methodology using Image Processing for measuring the severity of this disease in plant pathology. The image acquisition of infected leaves is done in the first stage then the images are pre-processed. Histogram is used for colour feature extraction The Edge detection methodology is used for infected area measurement and the results are fed to disease classifier to identify the stage of disease. This helps the plant pathologist in preparing consultative module to eradicate the disease completely.