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Defect pattern detection and classification are challenging for thin-film-transistor liquid-crystal display (TFT-LCD) manufacturing. Limitations of the existing solutions for automatic optical inspection can be traced in part to the lack of a framework within which different existing and new defect patterns can be analyzed, while integrating domain knowledge and effective technologies. This study aims to develop a framework for image-based defect classification that employs the convolution neural networks without using complex and time-consuming image-processing processes in advance. An empirical study was conducted in a leading TFT-LCD manufacturing in Taiwan for validation. The results have shown that the defect patterns can be effectively classified by the proposed convolutional neural networks that outperform the existing approaches such as Support Vector Machine and Random Forest. The developed solution is implemented to effectively support the engineers. [ABSTRACT FROM AUTHOR]

• Plant leaf disease recognition. • Detection of Tomato leaf disease detection. • Ensemble classification for plant leaf disease recognition. • Diversity in ensemble classification. Plant health and accurate and timely diagnosis of plant diseases are of great importance for human health and the economy of the farmers in the world. Therefore, researchers in the community are looking for fast and accurate methods for diagnosing and classifying different types of plants and their diseases. However, analyzing and classifying plant diseases can be challenged due to various diseases and farm conditions. This study aims to analyze and classify 13 classes of tomato disease in the farm and laboratory conditions with high accuracy and speed through ensemble classification. Therefore, 260 ensemble classifiers were designed using various preprocessing, different feature extraction, and different classifiers. Then, we compared the effectiveness of those ensemble methods to reach the optimal ensemble classifiers. Two databases, including the Plantvillage and Taiwan tomato leaves, were used for evaluating the accuracy and precision of the proposed method in the laboratory and field conditions with different challenges. The best ensemble classifier was able to classify based on the conditions of the shadow, brightness changes, disease similarity, background clutter, multiple leaves, and different textures with 95.98% accuracy. Moreover, a comparison was made between the several Deep learning models and 260 proposed ensemble models in the proposed method. In addition, the performance of the proposed method was better compared to the state-of-the-art Deep learning model. [ABSTRACT FROM AUTHOR]