Rice leaf diseases prediction using deep neural networks with transfer learning

Krishnamoorthy, N.; Prasad, L V Narasimha; Kumar, C. S. Pavan; Subedi, Bharat; Abraha, Haftom Baraki; Sathishkumar, V E

doi:10.1016/j.envres.2021.111275

Cited by 171 publications

(38 citation statements)

References 17 publications

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“…Due to its ability to work with small data, it is currently quite popular in DL. The author showed a pre-trained DCNN InceptionRes-NetV2 using a TL strategy by integrating several fine-tuning hyperparameters; the final accuracy was 95.67% [26]. The study showed a VGG19-based TL algorithm for accurately detecting and diagnosing healthy leaves and five different diseases, including narrow brown spots, leaf scalds, leaf blasts, brown spots, and bacterial leaf blight.…”

Section: Related Workmentioning

confidence: 99%

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

et al. 2023

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Plant disease is a significant health concern among all living creatures. Early diagnosis can help farmers takenecessary steps to cure the disease and accelerate the production rate efficiently. Our research has beenconducted with five most common rice leaf diseases, such as bacterial leaf blight, brown spot, leaf blast,leaf scald, and narrow brown spot, including healthy class, and two categories of betel leaf, such as healthyand unhealthy class. A robust new deep ensemble model, based on InceptionResNetV2, EfficientNetV2L,and Xception, has been proposed, known as, PlantDet, in this research. PlantDet solves not only underfitting problems but also leverage nourished performances simultaneously for scarce dataset of the sparse number of different background image dataset. PlantDet integrates efficient data augmentation, preprocessing, Global Average Pooling layer, Dropout mechanism, L2 regularizers, PRelu activation function, Batch Normalization layers, and more Dense layers that make the model more robust compared to all existing models and help to handle underfitting and overfitting problems while maintaining high performance. PlantDet exceeds the previous state-of-art model for the Rice Leaf dataset with an Accuracy of 98.53%, a Precision of 98.50%, a Recall of 98.35%, a F1 of 98.42% and a Specificity of 99.71%. In addition, for the Betel Leaf dataset, PlantDet also surpassed all existing base models, including several robust ensemble models. Finally, Grad-CAM and Score-CAM have been accomplished with the Xception method to explain the model performances particularly to elaborate how the Deep Learning (DL) models works for this complex dataset. Score-CAM slightly outperformed Grad-CAM++ in terms of localizing the predicted area.

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Section: Related Workmentioning

confidence: 99%

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

et al. 2023

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“…Experimental evaluations on both lab data sets and actual cultivation reveal an overall classification accuracy of 98.75%. In [ 56 ], authors presented disease prediction from Rice leaves using transfer learning based on InceptionResNetV2. Chen et al [ 57 ] also demonstrated the identification of Rice plant diseases using transfer learning.…”

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

253

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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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“…Over the past decade, the deep learning approach has been considered more reliable due to its capability of learning visual features. Moreover, transfer learning is a collection of fine-tuned methods that allow for the development of high precision frameworks on more constrained specialized datasets, such as those pertaining to plant diseases [29]. It is perceived that the fine-tuning approach is superior to a CNN model that is trained from scratch.…”

Section: A State Of the Art Approaches In The Identification Of Crop ...mentioning

confidence: 99%

Rice Transformer: A Novel Integrated Management System for Controlling Rice Diseases

Patil

Kumar

2022

IEEE Access

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Rice disease classification is vital during the cultivation of rice crops. However, rice diseases were initially detected by visual examination from agricultural experts. Later the detection process progressed to automation, which involved images. The images captured lead to a lack of supporting information. The traditional approaches are less accurate when used with real time images. To address this limitation, a novel Rice Transformer is proposed in the paper that merges inputs from agricultural sensors and image data captured from the fields simultaneously. The proposed system consists of two branches: the sensor and image branches. Specifically, the attention approach is employed to extract the features from both modalities. Later, the extracted features are sent to the cross-attention module as input in a crisscross fashion, enhancing the ability to identify the features specific to rice diseases. The extracted features are further pooled, merged, and later passed through the Softmax classifier to classify the rice disease precisely. The dataset collected is a customized dataset with 4200 samples collected on a real-time basis from rice farms. The experiments conducted on the dataset represent that the proposed approach outperforms all the other fusion and attention models considered for comparison in this paper. The ablation analysis and performance metrics are measured to determine the effectiveness of the proposed system. The results achieved are quite promising as the proposed Rice transformer model achieves an accuracy of 97.38% for controlling rice disease.

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Rice leaf diseases prediction using deep neural networks with transfer learning

Cited by 171 publications

References 17 publications

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Rice Transformer: A Novel Integrated Management System for Controlling Rice Diseases

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