IT engineer, Jigjiga University, Ethiopia
Mr. Taye Mengistu is an innovative researcher and lecturer based at Jigjiga University, Ethiopia. With a strong foundation in computer science and information technology, he is making notable strides in applying machine learning to real-world challenges. His pioneering work includes utilizing ensemble convolutional neural networks (CNNs) for the classification of mango diseases, a significant advancement in agricultural technology aimed at enhancing crop health and productivity.
Profile
Google Scholar
Education š
Mr. Taye Mengistu holds a Master’s degree in Computer Science from Jimma University, Ethiopia, where he graduated in January 2022 with a GPA of 3.58. Prior to this, he completed his Bachelor’s degree in Information Technology at Jigjiga University, Ethiopia, graduating in July 2017 with a GPA of 3.84. His strong academic background underscores his deep understanding and expertise in his field.
Experience š¢
Mr. Taye Mengistu has been serving as a Lecturer at Jigjiga University, Ethiopia, since October 2017. With over five years of experience, he has played a crucial role in both teaching and research. His responsibilities include delivering lectures, supervising student projects, and conducting research in various domains. His tenure at the university reflects his dedication to academic excellence and his ongoing commitment to advancing knowledge in his field.
Classification of Mango Disease Using Ensemble Convolutional Neural Network
The classification of mango diseases using ensemble convolutional neural networks (CNNs) represents a cutting-edge research area at the intersection of agriculture and artificial intelligence. This research focuses on leveraging advanced machine learning techniques to accurately identify and categorize diseases affecting mango crops, which is crucial for improving agricultural productivity and sustainability.
Key Aspects of Research Interests:
Ensemble Convolutional Neural Networks (CNNs): Utilizing ensemble methods to combine multiple CNN models to enhance classification accuracy. This approach improves the robustness and reliability of disease detection systems by aggregating predictions from different models.
Disease Classification: Developing algorithms to classify various mango diseases based on visual symptoms captured in images. Accurate classification helps in timely diagnosis and effective management strategies, minimizing crop loss and ensuring better yield.
Image Processing and Analysis: Applying image processing techniques to preprocess and analyze mango leaf and fruit images. This includes noise reduction, feature extraction, and image augmentation to improve model performance.
Machine Learning in Agriculture: Exploring the application of machine learning models to agricultural problems, particularly in disease detection and management. This research aims to bridge the gap between AI technology and practical agricultural needs.
Sustainable Agriculture: Enhancing disease management practices to promote sustainable agriculture. By accurately classifying and managing diseases, farmers can reduce the reliance on chemical treatments, leading to more eco-friendly farming practices.
Awards š
Certification in HDP, Jigjiga University (May 2022) – Recognized for his advanced skills and knowledge in his field.
Publications š
Classification of mango disease using ensemble convolutional neural network– JSmart Agricultural Technology 2024. link