A novel framework for soybean leaves disease detection using DIM-U-net and LSTM

Srilakshmi, A.; Geetha, K.

doi:10.1007/s11042-023-14775-6

Cited by 3 publications

(3 citation statements)

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“…Several studies have employed various forms of the Unet architectures to identify and categorize plant leaf diseases. For example, researchers in [3] proposed a novel method for detecting leaf diseases in soybeans. Their framework utilised a DIM UNet to extract features and an LSTM to classify.…”

Section: Related Workmentioning

confidence: 99%

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Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and Classification

Abinaya,

Kumar,

Alphonse

2023

IEEE Access

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Plant leaf diseases pose a significant threat to global food security and cause substantial economic losses. The objective of this study is to develop an effective approach for early detection and accurate identification of plant leaf diseases using computer vision techniques. The proposed method, Cascading Autoencoder with Attention Residual U-Net (CAAR-UNet), leverages deep learning to achieve precise segmentation and classification of plant leaf diseases. By cascading Symmetric Autoencoders with Attention Residual U-Net model and training on a custom dataset, it surpassed existing methods in identifying four disease classes. The model achieves remarkable accuracy, with a mean pixel accuracy of 95.26% and a weighted mean intersection over union of 0.7451, accurately capturing individual pixels and delineating disease class boundaries. This approach holds great potential in facilitating early plant disease detection and improving crop management practices. Its adoption can significantly impact food security worldwide, addressing a critical gap in the agricultural sector. The results highlight the effectiveness of the proposed strategy in plant disease management and open the door for further research in this field.

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Section: Related Workmentioning

confidence: 99%

“…The last decoder block is subsequently a 1x1 convolutional layer with a softmax activation function that produces a segmentation mask with a number of class channels. The forward flow of each block is represented in (3).…”

Section: ) Symmetric Autoencoder (Sae)mentioning

confidence: 99%

Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and Classification

Abinaya,

Kumar,

Alphonse

2023

IEEE Access

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“…It is important to emphasize, however, that the high classification accuracies are almost always obtained on data with rather limited variability, so the reported results are not necessarily valid for real practical conditions (this issue is addressed in more depth in the next section). Disease recognition RGB DenseNet201 0.97 1 [23] Water stress detection RGB AlexNet, GoogLeNet, Inception V3 0.93 1 [24] Root phenotyping RGB CAE 0.66-0.99 [15] Weed detection RGB JULE, DeepCluster 0.97 1 [11] Volunteer corn detection RGB, CIR GoogleNet 0.99 1 [25] Disease severity RGB FPN, U-Net, DeepLabv3+ 0.95-0.98 [3] Pest detection HS Attention-ResNet 0.95 1 [26] Stem phenotyping RGB YOLO X 0.94 1 [27] Pod detection, yield prediction RGB YOLO v5 0.94 4 [28] Disease recognition RGB DCNN 0.98 1 [29] Seed counting RGB Two-column CNN 0.82-0.94 [30] Pest detection RGB Modified YOLO v4 0.87 2 [31] Disease severity RGB RetinaNet 0.64-0.65 1,2 [32] Weed detection RGB Faster R-CNN, YOLO v3 0.89-0.98 [19] Defoliation estimation RGB, synthetic AlexNet, VGGNet and ResNet 0.98 3 [33] Disease recognition RGB DIM-U-Net, SR-AE, LSTM 0.99 2 [34] Weed detection RGB DCNN 0.93 1 [35] Pest detection RGB Several CNNs 0.94 1 [12] Seed-per-pot estimation RGB DCNN 0.86 1 [36] Cultivar identification RGB ResNet-50, DenseNet-121, DenseNet 0.84 1 [37] Disease recognition RGB AlexNet, GoogLeNet, ResNet-50 0.94 1 [38] Pod counting RGB YOLO POD 0.97 4 [39] Seed phenotyping RGB, synthetic Mask R-CNN 0.84-0.90 [40] Yield prediction, biomass HS DCNN 0.76-0.91 [41] Disease recognition RGB GAN 0.96 1 [42] Disease recognition RGB Faster R-CNN 0.83 5 [43] Weed detection RGB Faster R-CNN 0.99 1 [44] Pod counting RGB R-CNN, YOLO v3, YOLO v4, YOLO X 0.90-0.98 [45] Seed defect recognition RGB MobileNet V2 0.98 1 [46] Seed counting RGB P2PNet-Soy 0.87 4 [47] Cultivar identification HS DCNN 0.90 1 …”

Section: Proximal Images As Main Input Datamentioning

confidence: 99%

Deep Learning for Soybean Monitoring and Management

Barbedo

2023

Seeds

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Artificial intelligence is more present than ever in virtually all sectors of society. This is in large part due to the development of increasingly powerful deep learning models capable of tackling classification problems that were previously untreatable. As a result, there has been a proliferation of scientific articles applying deep learning to a plethora of different problems. The interest in deep learning in agriculture has been continuously growing since the inception of this type of technique in the early 2010s. Soybeans, being one of the most important agricultural commodities, has frequently been the target of efforts in this regard. In this context, it can be challenging to keep track of a constantly evolving state of the art. This review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis of the main challenges and research gaps that still remain. The ultimate goal is to facilitate the leap from academic research to technologies that actually work under the difficult conditions found in the the field.

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