A novel approach to classify and detect bean diseases based on image processing

Abed, Sa’ed; Esmaeel, Anwar Ali

doi:10.1109/iscaie.2018.8405488

Cited by 21 publications

(7 citation statements)

References 9 publications

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“…The author has proposed a novel technique for two kinds of plant diseases that includes linear contrast stretching using min-max to enhance the quality of the image, segmentation using K-Means to extract regions of interest, GLCM for feature extraction, and a SVM for bean disease classification [7]. In paper [8], multiple descriptors are used for the identification of bean leaf disease using CNN.…”

Section: Related Workmentioning

confidence: 99%

Parallel Custom Deep Learning Model for Classification of Plant Leaf Disease Using Fusion of Features

2024

IJIES

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Machine learning models have been used to protect the plant from leaf disease by early detection and classification in the field of agriculture. Farmers' principal source of income is agriculture, and plant leaf disease causes them to lose a lot of crop each year. It is essential to provide food for the whole world by detecting disease at an early stage in the farm for future decision-making. However, there is currently no such kind of system in place to forecast a disease at an early stage. The primary goal of this research is to inform farmers about a novel strategy for preventing leaf disease in plants. Through this research and earlier diagnosis, the losses incurred by farmers might be decreased. In this study, we have suggested a unique custom parallel deep convolutional neural network-based model for the classification of leaf diseases on the farm. This proposed CNN model is trained using the plant village dataset with 10 different categories of leaf disease classes. The patterns of leaf images at particular times are utilized in conjunction with computer vision techniques to detect plant leaf diseases. The tomato plant is taken into consideration for current scientific efforts in disease identification, categorization, and diagnosis. Image resizing, cropping, segmentation using k-means clustering and thresholding, and normalization are five different types of data preprocessing techniques used. The suggested approach may accurately diagnose diseases by automatically extracting features using implicit CNN. During comparisons with the testing of the given data, the output of the proposed model is 99.65%, which is better than transfer learning models available in the literature. This suggested model may be checked for consistency and reliability, and farmers can use it as a practical tool to protect their leaf plants from any kind of disease.

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Section: Related Workmentioning

confidence: 99%

Parallel Custom Deep Learning Model for Classification of Plant Leaf Disease Using Fusion of Features

2024

IJIES

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“…Mean and median filters are used to remove noise from images [79]. To sharpen the image details, Gaussian and Laplacian filters are applied [80,81]. Moreover, augmentation is a technique involving artificially increasing more data by applying various augmenting techniques to existing datasets [82].…”

Section: Image Preprocessingmentioning

confidence: 99%

Deep Learning for Plant Disease Detection and Classification: A Systematic Analysis and Review

Das,

Rupa,

Pumrin

et al. 2024

Curr. Appl. Sci. Technol.

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Detection and classification of leaf and crop diseases in a traditional way is a very laborious task as it involves a significant amount of physical work, huge expert manpower, and valuable time. Automatic systems are more accurate and require less time, labor, and physical work. Artificial intelligence and deep learning-based systems can help in the rapid detection and classification of plant leaf and crop diseases as they occur and help to reduce the hostile effects of disease on food security and the economy. In this systematic and state-of-the-art review, an in-depth study was performed to find and assess the use of different deep learning methods in leaf disease detection and classification. In this study, we exhaustively reviewed contemporary research work on leaf and plant disease detection and classification using deep learning methods performed by several researchers worldwide. Various deep-learning techniques with intermediate steps, public datasets, types of diseases detected and classified, types of plants used, performance metrics used to evaluate models, and achieved results are summarized. Finally, various challenges encountered in using deep learning methods were summarized along with some guidelines that will be helpful for future researchers in this area.

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“…The model has achieved 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. Abed and Esmaeel studied the detection of powdery mildew and bacterial brown spot diseases on bean leaves [14]. It's indicated that the developed methodology successfully detected the two types of leaf diseases with an accuracy of 100%.…”

Section: Literature Reviewmentioning

confidence: 99%

Disease detection in bean leaves using deep learning

SERTTAŞ

Deniz

2023

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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The care and health of agricultural plants, which are the primary source for people to eat healthily, are essential. Disease detection in plants is one of the critical elements of smart agriculture. In parallel with the development of artificial intelligence, advancements in smart agriculture are also progressing. The development of deep learning techniques positively affects smart farming practices. Today, using deep learning and computer vision techniques, various plant diseases can be detected from images such as photographs. In this research, deep learning techniques were used to detect and diagnose bean leaf diseases. Healthy and diseased bean leaf images were used to train the convolutional neural network (CNN) model, which is one of the deep learning techniques. Transfer learning was applied to CNN models to detect plant diseases with the difference of related works. A transfer learning-based strategy to identify various diseases in plant varieties is demonstrated using leaf images of healthy and diseased plants from the Bean dataset. With the proposed method, 1295 images were studied. The results show that our technique successfully identified disease status in bean leaf images, achieving an accuracy of 98.33% with the ResNet50 model.

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A novel approach to classify and detect bean diseases based on image processing

Cited by 21 publications

References 9 publications

Parallel Custom Deep Learning Model for Classification of Plant Leaf Disease Using Fusion of Features

Parallel Custom Deep Learning Model for Classification of Plant Leaf Disease Using Fusion of Features

Deep Learning for Plant Disease Detection and Classification: A Systematic Analysis and Review

Disease detection in bean leaves using deep learning

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