A detection of tomato plant diseases using deep learning MNDLNN classifier

Bora, Rina; Parasar, Deepa; Charhate, Shrikant

doi:10.1007/s11760-023-02498-y

Cited by 15 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the model fails to autonomously adapt to disturbances caused by changes in field lighting, leaf distortion, and variations in lesion angles and poses, resulting in poor performance in natural environments. The system proposed by Bora et al (2023) [ 29 ] achieved disease detection rates of 99.84%, 95.2%, 96.8%, and 93.6% for tomato leaves, stems, fruits, and root positions, respectively. Zhang et al (2023) [ 30 ] reported experimental results on 3123 tomato leaf images, including 1850 camera-captured images and 1273 obtained from the internet, indicating that the proposed M-AORANet achieved a recognition accuracy of 96.47%.…”

Section: Introductionmentioning

confidence: 99%

An efficient deep learning model for tomato disease detection

Wang,

Liu

2024

Plant Methods

View full text Add to dashboard Cite

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR’s self-attention mechanism to craft a backbone feature extraction network, enhancing the model’s capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network’s ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

An efficient deep learning model for tomato disease detection

Wang,

Liu

2024

Plant Methods

View full text Add to dashboard Cite

show abstract

Monitoring of impurities in green peppers based on convolutional neural networks

Zhang

et al. 2023

SIViP

View full text Add to dashboard Cite

Various tomato infection discrimination using spectroscopy

Ruszczak,

Smykała,

Tomaszewski

et al. 2024

SIViP

View full text Add to dashboard Cite

Diagnosing plant diseases is a difficult task, but it could be made easier with the use of advanced instrumentation and the latest machine learning techniques. This paper is a further development of a previous authors study by the authors, which has been extended to provide the classification method for tomato diseases and to indicate the spectral ranges of greatest importance for this process. As tomatoes are one of the most popular and consumed vegetables, and diseases of this crop even reduce yields by up to 80% every year, their detection is a vague topic. This manuscript describes research in which spectroscopy was used to develop methods for discriminating between selected tomato diseases. The following, frequently occurring diseases were investigated for this research: anthracnose, bacterial speck, early blight, late blight, and Septoria Leaf Spot. The study used a dataset consisting of 3877 measurements taken with the ASD FieldSpec 4 Hi-Res spectroradiometer in the 350–2500 nm range from 2019/09/10 to 2019/12/20. The highest classification efficiency ($$F_1$$ F 1 score) of 0.896 was obtained for the logistic regression based model which was evaluated on Septoria Leaf Spot disease records.

show abstract

A detection of tomato plant diseases using deep learning MNDLNN classifier

Cited by 15 publications

References 20 publications

An efficient deep learning model for tomato disease detection

An efficient deep learning model for tomato disease detection

Monitoring of impurities in green peppers based on convolutional neural networks

Various tomato infection discrimination using spectroscopy

Contact Info

Product

Resources

About