Potato Leaf Disease Classification Based on Distinct Color and Texture Feature Extraction

Suttapakti, Ungsumalee; Bunpeng, Aekapop

doi:10.1109/iscit.2019.8905128

Cited by 30 publications

(3 citation statements)

References 4 publications

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“…A machine learning-based automated approach ( Suttapakti and Bunpeng, 2019 ) for classifying potato leaf diseases was introduced in a separate study. The maximum-minimum color difference technique was used alongside a set of distinctive color attributes and texture features to create this system.…”

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

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Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

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Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…al. (2019), [16] it is essential to classify the potato leaf disease in order to prevent crop loss. Image processing methods were used to automatically classify potato leaf diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Feature Fusion Ensemble of VGG16 and ResNet-50 for Automated Potato Leaf Abnormality Detection in Precision Agriculture

Trivedi,

Mahajan,

Maheshwari

et al. 2023

Preprint

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In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focused on disease due to different types of microbial infection in potato leaves using computer vision and machine learning approaches. In this paper, a data science approach for multiclass classification of potato normal and abnormal leaves due to fungal infection like early blight and late blight is performed using the ensembling of deep learning (DL) CNN models. Firstly, the performance of classification on potato disease is verified separately on VGG16 and ResNet−50 CNN models after pre-processing of the leaf dataset. The pre-processing includes noise removal and normalization. Further improvement in classification accuracy is achieved by the ensembling of VGG16 and ResNet−50 CNN models. The ensembling of CNN models is performed on the feature level by fusing features extracted using VGG16 and ResNet−50. From the experimental results, performed on publicly available datasets consisting of 2152 number of normal and abnormal images it is observed that the average classification accuracy of 98.22%, 96.16% and 95.68% is achieved using the proposed ensemble, VGG16 and ResNet−50 models respectively. The efficacy of the proposed approach (ensemble technique at feature level fusion) is verified in comparison with recently reported DL model-based approaches.

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“…Penanganan penyakit pada daun tanaman ini perlu dilakukan secara cepat dan tepat untuk mencegah penurunan produksi tanaman kentang [8]. Permasalahan ini dapat diatasi dengan pemanfaatan image processing yang dapat digunakan untuk mendeteksi perbedaan jenis daun dengan menggunakan data citra yang telah dikumpulkan sebagai rujukannya [9].…”

Section: Gambar 1 Penyakit Pada Daun Kentang (A) Early Blight (B) Late Blightunclassified

Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

Rozaqi¹,

Arief

Sunyoto

2021

PELS

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Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

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Potato Leaf Disease Classification Based on Distinct Color and Texture Feature Extraction

Cited by 30 publications

References 4 publications

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Leveraging Feature Fusion Ensemble of VGG16 and ResNet-50 for Automated Potato Leaf Abnormality Detection in Precision Agriculture

Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

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