Machine Learning Based Identification of Tomato Leaf Diseases at Various Stages of Development

Gadade, H. D.; Kirange, D. K.

doi:10.1109/iccmc51019.2021.9418263

Cited by 15 publications

(4 citation statements)

References 13 publications

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“…Many studies 13‐15 have used disease leaf images of tomatoes from the PlantVillage data set for tomato disease‐related studies, which is sufficient to confirm the reliability of this dataset.…”

Section: Methodsmentioning

confidence: 98%

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A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Jing

Qiao

et al. 2023

J Sci Food Agric

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BACKGROUNDTomato is one of the most important vegetables in the world. Timely and accurate identification of tomato disease is a critical way to ensure the quality and yield of tomato production. The convolutional neural network is a crucial means of disease identification. However, this method requires manual annotation of a large amount of image data, which wastes the human cost of scientific research.RESULTSTo simplify the process of disease image labeling and improve the accuracy of tomato disease recognition and the balance of various disease recognition effects, a BC‐YOLOv5 tomato disease recognition method is proposed to identify healthy growth and nine types of diseased tomato leaves. In the present study, the YOLOv5 model is improved by designing an automatic tomato leaf image labeling algorithm, using the weighted bi‐directional feature pyramid network to change the Neck structure, adding the convolution block attention module, and changing the input channel of the detection layer. Experiments show that the BC‐YOLOv5 method has an excellent image annotation effect on tomato leaves, with a pass rate exceeding 95%. Furthermore, compared with existing models, the performance indices of BC‐YOLOv5 to identify tomato diseases are the best.CONCLUSIONBC‐YOLOv5 realizes the automatic labeling of tomato leaf images before the start of training. This method not only identifies nine common tomato diseases, but also improve the accuracy of disease identification and have a more balanced identification effect on various diseases. It provides a reliable method for the identification of tomato disease. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 98%

“…Precision and recall calculation formulas are shown in Eqns ( 13) and (14). TP represents true positives, FP represents false positives and FN represents false negatives.…”

Section: Evaluation Indicesmentioning

confidence: 99%

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Jing

Qiao

et al. 2023

J Sci Food Agric

View full text Add to dashboard Cite

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“…They collected a dataset of over two thousand images, which was split into a training set and a test set in a 7:3 ratio, ultimately achieving a 100% accuracy rate on the test set, but the tiny dataset size limits the model's applicability. Gadade Haridas D. et al [9] introduced a machine-learning-based method for classifying diseases on tomato leaves. They compared methods such as Support Vector Machine (SVM) and Random Forest, demonstrating their model's robustness, though this approach is not highly efficient.…”

Section: Introductionmentioning

confidence: 99%

Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques

Chen,

Lang,

et al. 2024

Agriculture

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An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention mechanism backbone network, an aligned-head module, a Transformer-based semantic segmentation network, and a specially designed alignment loss function. The integration of these technologies not only optimizes the model’s ability to capture subtle disease features but also improves the efficiency of integrating sensor and image data, further enhancing the accuracy of the segmentation tasks. Experimental results demonstrate the superiority of this framework. For disease detection, the proposed method achieved a precision of 94%, a recall of 92%, and an accuracy of 92%, surpassing classical models like AlexNet, GoogLeNet, VGGNet, ResNet, and EfficientNet. In lesion segmentation tasks, the proposed method achieved a precision of 95%, a recall of 90%, and an mIoU of 94%, significantly outperforming models such as SegNet, UNet, and UNet++. The introduction of the aligned-head module and alignment loss function provides an effective solution for processing images lacking sensor data, significantly enhancing the model’s capability to process real agricultural image data. Through detailed ablation experiments, the study further validates the critical role of the aligned-head module and alignment loss function in enhancing model performance, particularly in the attention-head ablation experiment where the aligned-head configuration surpassed other configurations across all metrics, highlighting its key role in the overall framework. These experiments not only showcase the theoretical effectiveness of the proposed method but also confirm its practical value in agricultural disease management practices.

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“…However, these methods generally have the disadvantage of being more subjective, time-consuming, inefficient, and difficult to achieve in real-time monitoring [4]. In recent years, with the rapid development of computer technology, an increasing number of scholars have utilized techniques such as computer vision and machine learning to identify crop leaf features for rapid detection and classification of different crop diseases [5][6][7][8][9]. Poornima et al [10] utilized image processing and machine learning techniques to analyze and detect plant diseases and achieved early and periodic detection.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the YOLOv5 Model in the Optimization of the Brown Spot Disease Recognition Algorithm of Kidney Bean

Su,

Li,

Wang

et al. 2023

Plants

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The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.

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Machine Learning Based Identification of Tomato Leaf Diseases at Various Stages of Development

Cited by 15 publications

References 13 publications

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques

Improvement of the YOLOv5 Model in the Optimization of the Brown Spot Disease Recognition Algorithm of Kidney Bean

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