Maize Diseases Image Identification and Classification by Combining CNN with Bi-Directional Long Short-Term Memory Model

Hasan, Md. Jahid; Alom, Shahin; Dina, Umme Fatema; Moon, Mahmudul Hasan

doi:10.1109/tensymp50017.2020.9230796

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…Furthermore, the input dataset is crucial for training CNNs, because it is the basic source of information. By providing geometrically transformed replicates of the sample images to provide a larger and more general dataset, the accuracies of CNNs were improved [55,89,90]. In addition, image quality can interfere with crop phenotyping detection results.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

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Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

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Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

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“…In addition, the lightweight CNNs cannot encode context dependencies and spatial structure information between different image regions. Furthermore, the extracted features cannot improve their image representation ability [4].To overcome this limitation, the ConvLSTM network structure and the Squeeze-and-Excitation (SE) module can be introduced. ConvLSTM, as a structure that combines the features of convolutional and Long Short-Term Memory (LSTM) networks, is able to efficiently deal with temporal-spatial dependencies in image sequences.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Lightweight Network and Choquet Fuzzy Ensemble Technology for Soybean Disease Identification

Hang,

Meng,

2024

IEEE Access

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The identification of soybean disease images in natural scenes has been a challenging task due to their complex backgrounds and diverse spot patterns. Traditional single convolutional neural network (CNN) for soybean disease image recognition often cannot have both high identification accuracy and strong generalization ability. Therefore, this paper focuses on the classification of soybean leaf diseases using improved lightweight networks for transfer learning, and improves the identification accuracy and precision by introducing Choquet fuzzy ensemble strategy. First, the convolutional long short-term memory (ConvLSTM) layer and the squeeze and excitation (SE) block are introduced into the four original lightweight models (Xception, MobileNetV2, NASNetMobile, MobileNet) to improve the network's ability to grasp image features, and then the classification confidence scores obtained from the improved lightweight networks are fed into the fuzzy iensemble network to complete the aggregation of the final results. In order to improve the performance of the model and enrich the distribution of samples in the highdimensional feature space, this paper converts soybean healthy leaf images to diseased leaf images using an unsupervised image translation method based on Cycle-Consistent Adversarial Networks (CycleGAN). The results show that the improved lightweight model has higher recognition accuracy than the original network. The proposed fuzzy ensemble model obtains 94.27% recognition accuracy and an average F1-score of 94% in the soybean leaf disease classification task, which is better than a single model and other ensemble methods. It has a good application prospect and initially meets the production requirements of soybean disease identification.INDEX TERMS Choquet integral; CycleGAN; Fuzzy ensemble; Soybean leaf diseases.

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“…In study [9] or the classification of diffuse liver disease based on ultrasound images with multimodal features, the method used can increase overall classification accuracy by 5.4%. Furthermore, research [10] conducted to classify disease symptoms in maize plants by combining CNN with Bidirectional Long Short-Term Memory model showed good results of 99.02%. Thus, the selection of this research is based on the success of the CNN method in previous studies and its potential in providing an accurate and effective solution to the problem of rice leaf disease classification.…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease

Hastari,

Winanda,

Pratama

et al. 2024

PREDATECS

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Rice is the most important crop in global food security and socioeconomic stability. A part of the world's population makes rice a food requirement but the problem is found that all rice varieties suffer from several diseases and pests. Therefore, it is necessary to ensure the quality of healthy and proper rice growth by detecting diseases present in rice plants and treatment of affected plants. In this study, the Convolutional Neural Network (CNN) algorithm was applied in classifying diseases on the leaves of rice plants by experimenting with several parameters and architecture to get the best accuracy. This study was conducted image classification of rice plant disease using CNN architecture ResNet-50V2 with data using preprocessing Augmentation. The test was conducted with three optimizers such as SGD, Adam, and RMSprop by combining various parameters, namely epoch, batch size, learning rate, and SGD and RMSprop optimizers. Division of image data with 70:30 ratio of training data and test data; 80:20; 90:10. From these results, it was found that Adam was the best optimizer in the 80:20 data division in this study with an accuracy level of 0.9992, followed by the SGD optimizer with an accuracy level of 0.9983, while the RMSProp optimizer was ranked third with an accuracy level of 0.9978.

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Maize Diseases Image Identification and Classification by Combining CNN with Bi-Directional Long Short-Term Memory Model

Cited by 10 publications

References 10 publications

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Application of Improved Lightweight Network and Choquet Fuzzy Ensemble Technology for Soybean Disease Identification

Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease

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