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Comparative Analytics On Chilli Plant Disease Using Machine Learning Techniques, Sai Abhishta Roy Seelam
Comparative Analytics On Chilli Plant Disease Using Machine Learning Techniques, Sai Abhishta Roy Seelam
Electronic Theses and Dissertations
This thesis concerns the detection of diseases in chilli plants using machine learning techniques. Three algorithms, viz., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), and their variants have been employed. Chilli-producing countries, India, Mexico, China, Indonesia, Spain, the United States, and Turkey. India has the world’s largest chilli production of about 49% (according to 2020). Andhra Pradesh (Guntur) is the largest market in India, where their varieties are more popular for pungency and color. This study classifies five kinds of diseases that affect the chilli, namely, leaf spot, whitefly, yellowish, healthy, and leaf curl. A …
Development Of Enhanced Weed Detection System With Adaptive Thresholding, K-Means And Support Vector Machine, Dheeman Saha
Development Of Enhanced Weed Detection System With Adaptive Thresholding, K-Means And Support Vector Machine, Dheeman Saha
Electronic Theses and Dissertations
This paper proposes a sophisticated classification process to segment the leaves of carrots from weeds (mostly Chamomile). In the early stages, of the plants’ development, both weeds and carrot leaves are intermixed with each other and have similar color texture. This makes it difficult to identify without the help of the domain experts. Therefore, it is essential to remove the weed regions so that the carrot plants can grow without any interruptions. The process of identifying the weeds become more challenging when both plant and weed regions overlap (inter-leaves). The proposed system addresses this problem by creating a sophisticated means …
Towards Closed-Loop Deep Brain Stimulation: Behavior Recognition From Human Stn, Soroush Niketeghad
Towards Closed-Loop Deep Brain Stimulation: Behavior Recognition From Human Stn, Soroush Niketeghad
Electronic Theses and Dissertations
Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the …