RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN

Alruwaili, Madallah; Ṣiddiqi, Muḥammad Zubair; Khan, Ashiq; Azad, Mohammad; Khan, Abdullah Aman; Alanazi, Saad

doi:10.3390/bioengineering9100565

Cited by 18 publications

(13 citation statements)

References 31 publications

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“…Faster R-CNN and YOLOv3 algorithms are generally improved according to the actual requirements. The improved Faster R-CNN tends to improve detection accuracy ( Alruwaili et al, 2022 ; Nawaz et al, 2022 ), and the improved YOLOv3 algorithm emphasizes the improvement of detection speed ( Liu and Wang, 2020 ; Tian et al, 2019 ). Hence, balancing and optimizing detection accuracy and detection speed plays a major role in plant disease identification.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Plant Diseases Identification Based on Deep Learning Methods

Gong¹,

Zhang²

2023

Plant Pathol J

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Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

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Section: Discussionmentioning

confidence: 99%

An Analysis of Plant Diseases Identification Based on Deep Learning Methods

Gong¹,

Zhang²

2023

Plant Pathol J

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“…Velumani et al [16] used Faster R-CNN to study plant density in the early stages and concluded that the super-resolution method showed significant improvement. Alruwaili et al [17] used fast R-CNN to study tomatoes, and the final results showed that the accuracy of the RTF-RCNN proposed in the study was as high as 97.42%, which is better than traditional methods. The most common research direction is using different methods to identify different crops and ultimately obtain relatively suitable methods for identifying various crops [18][19][20].…”

Section: Introductionmentioning

confidence: 88%

Research on Red Jujubes Recognition Based on a Convolutional Neural Network

Guo

et al. 2023

Applied Sciences

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Red jujube is one of the most important crops in China. In order to meet the needs of the scientific and technological development of the jujube industry, solve the problem of poverty, realize the backward advantage, and promote economic development, smart agriculture is essential. The main objective of this study was to conduct an online detection study of unpicked red jujubes in order to detect as many red jujubes in the picture as possible while minimizing the occurrence of overfitting and underfitting. Experiments were conducted using the Histogram of Oriented Gradients + Support Vector Machine (HOG+SVM) traditional detection method and the You Only Look Once version 5 (YOLOV5) and Faster R-CNN modern deep learning detection methods. The precision, recall, and F1 score were compared to obtain a better algorithm. The study also introduced the AlexNet model with the main objective of attempting to combine it with other traditional algorithms to maximize accuracy. Labeling was used to label the training images in YOLOV5 and Faster Regions with CNN Features (Faster R-CNN) to train the machine model so that the computer recognized these features when it saw new unlabeled data in subsequent experiments. The experimental results show that in the online recognition detection of red jujubes, the YOLOV5 and Faster R-CNN algorithms performed better than the HOG + SVM algorithm, which presents precision, recall, and F1 score values of 93.55%, 82.79%, and 87.84% respectively; although the HOG + SVM algorithm was relatively quicker to perform. The precision of detection was obviously more important than the efficiency of detection in this study, so the YOLOV5 and Faster R-CNN algorithms were better than the HOG + SVM algorithm. In the experiments, the Faster R-CNN algorithm had 100% precision, 99.65% recall, an F1 score of 99.82%, and 83% non-underfitting images for the recognized images, all of which were higher than YOLOV5′s values, with 97.17% recall, an F1 score of 98.56%, and 64.42% non-underfitting. In this study, therefore, the Faster R-CNN algorithm works best.

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“… Seetharaman and Mahendran (2022) proposed using a convolutional recurrent neural network for banana leaf disease detection. Alruwaili et al. (2022) proposed real-time faster region convolutional neural network (RTF-RCNN) for the real-time detection of tomato leaf diseases in video streams.…”

Section: Related Workmentioning

confidence: 99%

A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection

Dong,

Fuentes,

Yoon

et al. 2023

Front. Plant Sci.

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Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement restricts the model’s adaptability when encountering samples from unseen disease categories. Additionally, there is a challenge of knowledge degradation for these static learning settings, as the acquisition of new knowledge tends to overwrite the old when learning new categories. To overcome these limitations, this study introduces a novel paradigm for plant disease detection called open-world setting. Our approach can infer disease categories that have never been seen during the model training phase and gradually learn these unseen diseases through dynamic knowledge updates in the next training phase. Specifically, we utilize a well-trained unknown-aware region proposal network to generate pseudo-labels for unknown diseases during training and employ a class-agnostic classifier to enhance the recall rate for unknown diseases. Besides, we employ a sample replay strategy to maintain recognition ability for previously learned classes. Extensive experimental evaluation and ablation studies investigate the efficacy of our method in detecting old and unknown classes. Remarkably, our method demonstrates robust generalization ability even in cross-species disease detection experiments. Overall, this open-world and dynamically updated detection method shows promising potential to become the future paradigm for plant disease detection. We discuss open issues including classification and localization, and propose promising approaches to address them. We encourage further research in the community to tackle the crucial challenges in open-world plant disease detection. The code will be released at https://github.com/JiuqingDong/OWPDD.

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RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN

Cited by 18 publications

References 31 publications

An Analysis of Plant Diseases Identification Based on Deep Learning Methods

An Analysis of Plant Diseases Identification Based on Deep Learning Methods

Research on Red Jujubes Recognition Based on a Convolutional Neural Network

A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection

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