2,997 research outputs found
Classifying Barako coffee leaf diseases using deep convolutional models
This work presents the application of recent Deep Convolutional Models (DCM) to classify Barako leaf diseases. Several selected DCMs performed image classification tasks using Transfer Learning and Fine-Tuning, together with data preprocessing and augmentation. The collected dataset used totals to 4,667. Each labeled into four different classes, which included Coffee Leaf Rust (CLR), Cercospora Leaf Spots (CLS), Sooty Molds (SM), and Healthy Leaves (HL). The DCMs were trained using the partial 4,023 images and validated with the remaining 644. The classification results of the trained models VGG16, Xception, and ResNetV2-152 attained overall accuracies of 97%, 95%, and 91%, respectively. By comparing in terms of True Positive Rate (TPR), we found that Xception has the highest number of correct classifications of CLR, VGG16 with SM, and CLS, while ResNetV2-152 with the lowest TPR for CLR. The evaluated results indicate that the use of Deep Convolutional Models with an adequate amount of data, proper fine-tuning, preprocessing, and transfer learning can yield efficient classifiers for identifying several Barako leaf diseases. This work primarily contributes to the growing field of deep learning, specifically for helping farmers improve their diagnostic process by providing a solution that can automatically classify Barako leaf diseases
Machine Learning for Leaf Disease Classification: Data, Techniques and Applications
The growing demand for sustainable development brings a series of information
technologies to help agriculture production. Especially, the emergence of
machine learning applications, a branch of artificial intelligence, has shown
multiple breakthroughs which can enhance and revolutionize plant pathology
approaches. In recent years, machine learning has been adopted for leaf disease
classification in both academic research and industrial applications.
Therefore, it is enormously beneficial for researchers, engineers, managers,
and entrepreneurs to have a comprehensive view about the recent development of
machine learning technologies and applications for leaf disease detection. This
study will provide a survey in different aspects of the topic including data,
techniques, and applications. The paper will start with publicly available
datasets. After that, we summarize common machine learning techniques,
including traditional (shallow) learning, deep learning, and augmented
learning. Finally, we discuss related applications. This paper would provide
useful resources for future study and application of machine learning for smart
agriculture in general and leaf disease classification in particular
Artificial intelligence-based solutions for coffee leaf disease classification
Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf
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