Crop disease identification and interpretation method based on multimodal deep learning

Zhou, Ji; Li, Jiuxi; Wang, Chun‐Shan; Wu, Haiyuan; Zhao, Chunjiang; Teng, Guifa

doi:10.1016/j.compag.2021.106408

Cited by 55 publications

(19 citation statements)

References 8 publications

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“…Zhou et al (2021) presented a method to detect and interpret plant infections based on multimodal deep learning. The cucumber and tomato widespread invasive diseases were used as study subjects.…”

Section: Literature Surveymentioning

confidence: 99%

“…In the literature, many research papers have been published for automated plant leaf disease diagnosis based upon image processing and machine learning (Ahmed & Reddy, 2021; Bari et al, 2021; Chen et al, 2022; Hassan et al, 2021; Zhou et al, 2021). By utilizing deep learning, identification with categorization of plant leaf infections helps the farmers to identify the category of infection and to provide treatment to the plant to control the infections.…”

Section: Introductionmentioning

confidence: 99%

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Plant disease identification using contextual mask auto‐encoder optimized with dynamic differential annealed optimization algorithm

Prasannakumar,

Latha

2023

Microscopy Res & Technique

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Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error‐prone. Early detection of plant diseases with the aid of artificial intelligence and computer vision can decrease the effects of disease and help plants withstand the downsides of continuing surveillance. In this manuscript, plant disease identification using contextual mask auto‐encoder optimized with dynamic differential annealed optimization algorithm (PDI‐CMAE‐DDAOA) is proposed. The plant village dataset is used to collect the images. Then the image is fed to preprocessing. Using an adaptive self‐guided filter approach, the noise is removed from the input images during the pre‐processing phase. The result of the pre‐processing section serves as input for the feature extraction segment. Four statistical features, including mean, variance, entropy, and kurtosis, are recovered from the cosine similarity hidden Markov model (CSHMM). The contextual mask auto‐encoder (CMAE) is given the extracted features to accurately classify the healthy and unhealthy regions of the plant image. The issue of slow convergence affects the CMAE. However, it is noted that the CMAE converges more quickly with deep learning features than with texture features in this instance. The CMAE classifier generally does not exhibit any adaptation of optimization algorithms for determining the best parameters to ensure the precise classification of plant disease. Therefore, dynamic differential annealed optimization algorithm (DDAOA) is considered to enhance the CMAE classifier, which accurately distinguishes between healthy and diseased plants. The proposed PDI‐CMAE‐DDAOA is done in Python. The efficacy of PDI‐CMAE‐DDAOA is evaluated under some performance metrics, like accuracy, precision, sensitivity, F1‐score, specificity, error rate, receiver operating characteristic curve (ROC), computational time. The proposed method provides higher accuracy 23.34%, 34.33%, and 32.07%; higher sensitivity 36.67%, 36.33%, and 23.21%; higher F1‐score 46.67%, 57.56%, and 43.21%; higher specificity 56.67%, 67.56%, and 23.21% analyzed with existing models, like transfer learning‐based deep ensemble neural network for plant leaf infection recognition (PDI‐DENN), plant disease detection with hybrid model based on convolutional auto‐encoder and convolutional neural network (PDI‐CAE‐CNN), and automatic and reliable leaf disease finding depending on deep learning methods (PDI‐EN‐CNN), respectively.Research Highlights To find the plant disease at early stage. To present PDI‐CMAE‐DDAOA. To get better classification accuracy by extracting the optimal features with the help of efficient CSHMM. To minimize the error during classification process. To maximize high area under curve value.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plant disease identification using contextual mask auto‐encoder optimized with dynamic differential annealed optimization algorithm

Prasannakumar,

Latha

2023

Microscopy Res & Technique

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“…Therefore, it is crucial for agricultural production to detect crop diseases in a timely manner [1]. One of the more effective solutions is to predict and classify crop diseases by collecting images of crop growth and building a detection model [2], which can give timely and effective early warning and intervention to agricultural production, improve the efficiency of disease control, and reduce agricultural losses caused by diseases.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

et al. 2022

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Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

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“…Different prevalent diseases and insect pests will take place every year. More severely, it will result in more than 30% of the output loss (Zhou et al, 2021). The insufficient soybean plant protection procedure, the increase of fungus virus pathogen categories, and poor cultivation methods are causes for increasing the damage degree of soybean plant diseases and insect pests.…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Soybean Leaf Disease by Transfer Learning

Gong¹,

Chen²,

Liu³

2022

American Journal of Biochemistry and Biotechnology

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Soybean diseases and insect pests are important factors that affect the output and quality of soybeans, thus it is necessary to do correct inspection and diagnosis of them. For this reason, based on improved transfer learning, this study proposed a classification method for soybean leaf diseases. Firstly, leaves were segmented from the whole image after removing the complicated background images. Secondly, the data-augmented method was applied to amplify the separated leaf disease image dataset to reduce overfitting. At last, the automatically fine-tuning convolutional neural network (Autotun) was adopted to classify the soybean leaf diseases. The verification accuracy of the proposed method is 94.23, 93.51 and 94.91% on VGG-16, ResNet-34 and DenseNet-121 networks respectively. Compared with the traditional finetuning method of transfer learning, the results show that this method is better than the traditional transfer learning method.

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Crop disease identification and interpretation method based on multimodal deep learning

Cited by 55 publications

References 8 publications

Plant disease identification using contextual mask auto‐encoder optimized with dynamic differential annealed optimization algorithm

Plant disease identification using contextual mask auto‐encoder optimized with dynamic differential annealed optimization algorithm

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

Automatic Diagnosis of Soybean Leaf Disease by Transfer Learning

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