De-Noising Diseased Plant Leaf Image

Kaur, Navneet; Devendran,; Verma, Sahil; Kavita, .; Jhanjhi, N. Z.

doi:10.1109/iccit52419.2022.9711604

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Due to the scarcity of public datasets for apple leaf diseases, this paper collected the Kashmiri Apple Plant Disease Dataset (Kaur et al, 2022) and the public dataset for Plant Pathology 2020-FGVC7 (Raman et al, 2022).…”

Section: Data Collectionmentioning

confidence: 99%

ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases

Xu,

Wang

2023

Front. Plant Sci.

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Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under a complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing the Mobilenet-V3s basic block, which integrates the coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depth-wise convolution, and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared with yolov5s) on the test set and reducing the floating point of operations (FLOPs) to 6.1 G (a decrease of 9.7 G compared with yolov5s). In summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields.

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Section: Data Collectionmentioning

confidence: 99%

ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases

Xu,

Wang

2023

Front. Plant Sci.

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“…In recent years, there has been significant growth in predicting crop yields [ [13] , [14] , [15] , [16] , [17] ], identifying crop types [ 18 , 19 ], crop diseases identification [ 10 , [20] , [21] , [22] , [23] , [24] ], crop health assessment [ [25] , [26] , [27] ] through the application of ML and deep convolutional neural networks. An intelligent system called AgroConsultant was created by Doshi [ 28 ] to help farmers make informed decisions about which crop to cultivate based on their current environmental conditions such as soil type, thickness, pH, rainfall, and temperature.…”

Section: Introductionmentioning

confidence: 99%