Potato diseases detection and classification using deep learning methods

Arshaghi, Ali; Ashourian, Mohsen; Ghabeli, Leila

doi:10.1007/s11042-022-13390-1

Cited by 48 publications

(15 citation statements)

References 42 publications

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“…In this study ( Arshaghi et al, 2023 ), machine vision and AI identify defects in agricultural goods like potatoes. A CNN is employed in this study to classify potato diseases.…”

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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“…In this study ( Arshaghi et al, 2023 ), machine vision and AI identify defects in agricultural goods like potatoes. A CNN is employed in this study to classify potato diseases.…”

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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“…In the past, the traditional method for detecting rotten potatoes was manual inspection, which is labor-intensive and inefficient. Recently, there has been a lot of research focusing on identifying rotten potatoes automatically, including using computer vision [10][11][12], spectral technology [13,14], GC-MS [15,16], and GC-IMS [17,18]. However, rotten potatoes are always hidden in piles when potatoes are stacked together.…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

Lin,

Wei,

Chen

et al. 2024

Sensors

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The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

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“…he yield of any crop in reasonable quantity and quality is essential for any country to become economically stable [1,2]. Food is a necessity of human life, so there must be no gap between the supply and demand of food, which is only possible by growing sufficient amounts of crops, especially those that are used abundantly, directly or indirectly, like wheat, rice, corn, cotton, and vegetables because they are needed daily [3]. Food shortage is observed due to the attack of different pests and diseases on crops, bad weather conditions, and the timely detection and eradication of disease in plants [4,5].…”

Section: Introductionmentioning

confidence: 99%

Crops Leaf Disease Recognition From Digital and RS Imaging Using Fusion of Multi Self-Attention RBNet Deep Architectures and Modified Dragonfly Optimization

Haider,

Khan,

Nazir

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Globally, pests and plant diseases severely threaten forestry and agriculture. Plant protection could be substantially enhanced by using non-contact, extremely effective, and reasonably priced techniques for identifying and tracking pests and plant diseases across large geographic areas. Precision agriculture is the study of using other technologies, such as hyperspectral remote sensing (RS), to increase cultivation instead of traditional agricultural methods with less negative environmental effects. In this work, we proposed a novel deep-learning architecture and optimization algorithm for crop leaf disease recognition. In the initial step, a multilevel contrast enhancement technique is proposed for a better visual of the disease on the leaves of cotton and wheat. After that, we proposed three novel residual block and selfattention mechanisms named 3-RBNet Self, 5-RBNet Self, and 9-RBNet Self. After that, the proposed models are trained on enhanced images and later extracted deep features from the self-attention layer. The 5-RBNET Self and 9-RBNET Self performed well in terms of accuracy and precision rate; therefore, we did not consider the 3-RBNET Self for the next process. The dragonfly optimization algorithm is proposed for the best feature selection and applied to the self-attention features of 5-RBNET Self and 9-RBNET Self models to improve the classification performance further and reduce the computational cost. The proposed method is evaluated on two publically available crop disease images, such as the Cotton, Wheat, and EuroSAT datasets. For both crops, the proposed method obtained a maximum accuracy of 98.60 and 93.90%, respectively, whereas for the EuroSAT, the proposed method obtained an accuracy of 83.10%. Compared to the results with

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Potato diseases detection and classification using deep learning methods

Cited by 48 publications

References 42 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

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

Crops Leaf Disease Recognition From Digital and RS Imaging Using Fusion of Multi Self-Attention RBNet Deep Architectures and Modified Dragonfly Optimization

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