Peanut leaf disease identification with deep learning algorithms

Xu, Laixiang; Cao, Bingxu; Ning, Shiyuan; Zhang, Wenbo; Zhao, Fengjie

doi:10.1007/s11032-023-01370-8

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…Leveraging the PlantVillage dataset, comprising ninety images, the study employs a methodology centred on a Support Vector Machine (SVM) classifier. Impressively, the proposed model achieves an accuracy surpassing 95% on the PlantVillage dataset, showcasing the efficacy of the developed apple leaf disease recognition method ( Arathi and Dulhare, 2023 ; Bin Naeem et al., 2023 ; Xu et al., 2023 ). This work holds substantial promise for advancing computer vision applications in precision agriculture and plant health monitoring ( Vengaiah and Konda, 2023 ; Terentev et al., 2023 ; Liu et al., 2023 ).…”

Section: Literature Reviewmentioning

confidence: 72%

Detection and identification of plant leaf diseases using YOLOv4

Aldakheel,

Zakariah,

Alabdalall

2024

Front. Plant Sci.

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Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers’ dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model’s resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study’s results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.

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Section: Literature Reviewmentioning

confidence: 72%

Detection and identification of plant leaf diseases using YOLOv4

Aldakheel,

Zakariah,

Alabdalall

2024

Front. Plant Sci.

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“…The loss does not keep leveling off, but gradually increases [35]. The use of residual blocks allows the depth of the network to be further deepened under the premise of ensuring normal operation [36]. The residual operation is formulated as shown by Xu, et al [36].…”

Section: Residual Networkmentioning

confidence: 99%

Enhancing Corn Pest and Disease Recognition through Deep Learning: A Comprehensive Analysis

Xu,

Li,

Wang

et al. 2023

Agronomy

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Pests and diseases significantly impact the quality and yield of maize. As a result, it is crucial to conduct disease diagnosis and identification for timely intervention and treatment of maize pests and diseases, ultimately enhancing the quality and economic efficiency of maize production. In this study, we present an enhanced maize pest identification model based on ResNet50. The objective was to achieve efficient and accurate identification of maize pests and diseases. By utilizing convolution and pooling operations for extracting shallow-edge features and compressing data, we introduced additional effective channels (environment–cognition–action) into the residual network module. This step addressed the issue of network degradation, establishes connections between channels, and facilitated the extraction of crucial deep features. Finally, experimental validation was performed to achieve 96.02% recognition accuracy using the ResNet50 model. This study successfully achieved the recognition of various maize pests and diseases, including maize leaf blight, Helminthosporium maydis, gray leaf spot, rust disease, stem borer, and corn armyworm. These results offer valuable insights for the intelligent control and management of maize pests and diseases.

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“…Recently, in the agricultural sector, plant diseases have become a major factor that caused the reduction of productivity and loss of yield. To minimize these losses number of researchers have done several works to detect disease at an early stage by identifying the infected leaf automatically [14], [28], [29]. There are vast works done for the identification of plant diseases using image processing, machine learning approach, and deep learning approach.…”

Section: Related Workmentioning

confidence: 99%

Developing Common Bacteria Blight Disease Detection Model for Common Bean Using Deep Learning Approach

Senbato,

Ayalew

2023

Preprint

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In many farm locations in Ethiopia, common bacterial blight (CBB), one of the common bean's known diseases, impacts the crop, lowers yield by up to 45 percent of the production, and also has an impact on seed quality. Recently, many researchers have tried to overcome this disease by conducting field surveys, crop management activities, crop rotation, chemical treatment, proper cultural practices, and integrated disease management. However, this method is a very tedious, time-consuming, and costly technique that requires more experts. To overcome these issues, a modern deep learning approach is proposed for the early detection of common bean disease. The three main phases are followed for the proposed approach. Firstly, the collection of healthy and diseased common bean images with the help of domain experts from different agricultural research centers is done. Then the design of a modern convolutional neural network that can detect and classify the input image as diseased or healthy is done. Lastly, the designed model is trained and evaluated. During the classification, the designed model is assessed against the performance of two pre-trained models (VGG16 and InceptionV3) to accurately detect the common bean disease. The performance of the proposed model is evaluated using a dataset that contains total images of 3,135 with two classes of healthy and common bacterial blight disease. We have used data augmentation techniques to generate more images to fit the proposed model. Based on our experimental results, the best performance is achieved using a proposed model with a classification accuracy of 98.2% for early detection of common bacterial blight disease on a common bean leaf. The result of this study can help farmers and domain experts by detecting CBB disease at an early stage for the necessary treatments.

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Peanut leaf disease identification with deep learning algorithms

Cited by 10 publications

References 28 publications

Detection and identification of plant leaf diseases using YOLOv4

Detection and identification of plant leaf diseases using YOLOv4

Enhancing Corn Pest and Disease Recognition through Deep Learning: A Comprehensive Analysis

Developing Common Bacteria Blight Disease Detection Model for Common Bean Using Deep Learning Approach

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