Rice Disease Detection Based on Image Processing Technique

Rahman, Md. Atiqur; Shoumik, Md. Shahriar Nawal; Rahman, Md. Mahbubur; Hena, Most. Hasna

doi:10.1007/978-981-15-5224-3_13

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…This dataset consists of Bacteria leaf blight, Brown Spot, Brown Plant hopper, False Smut, and Stemberg images. CNN recognized the accuracy as 95% in disease detections and classifications [68].…”

Section: Comparative Analysis Of ML Methods In Disease Detectionsmentioning

confidence: 99%

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Hasan,

Uddin,

Akhond

et al. 2023

IJPB

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Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image processing and Machine Learning (ML) techniques are used to detect rice disease early and within a relatively brief time period. This article aims to demonstrate the performance of different ML algorithms in rice disease detection through image processing. We critically examined different techniques, and the outcomes of previous research have been reviewed to compare the performance of rice disease classifications. Performance has been evaluated based on the criteria of feature extraction, clustering, segmentation, noise reduction, and level of accuracy of disease detection techniques. This paper also showcases various algorithms for different datasets in terms of training methods, image preprocessing with clustering and filtering criteria, and testing with reliable outcomes. Through this review, we provide valuable insights into the current state of ML-based approaches for the early detection of rice diseases, and assist future research and improvement. In addition, we discuss several challenges that must be overcome in order to achieve effective identification of rice diseases.

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Section: Comparative Analysis Of ML Methods In Disease Detectionsmentioning

confidence: 99%

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Hasan,

Uddin,

Akhond

et al. 2023

IJPB

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show abstract

“…Among custom model based approaches, [23] proposes a MobileNet like architecture by incorporating attention mechanism and name it ADSNN-BO. A custom CNN architecture has also been proposed in [24], [26], [27]. Lately, [27] further utilizes Generative Adversarial Network (GAN) to generate synthetic data.…”

Section: Related Workmentioning

confidence: 99%

Towards Sustainable Agriculture: A Novel Approach for Rice Leaf Disease Detection Using dCNN and Enhanced Dataset

Bijoy,

Hasan,

Biswas

et al. 2024

IEEE Access

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Rice is one of the foremost food grains that dispenses sustenance to about half of the world's population. It is cultivated all over the world. The leaf disease detection of this crop is one of the chronic agricultural obstacles that farmers and planting experts have been struggling with for a long time. As a result of the leaf diseases, producing the amount of rice required to feed the world's rising population has become very challenging. Hence, automatically detecting rice leaf diseases is an inevitable task to increase productivity. Numerous deep learning based methods have been proposed for rice leaf disease detection, which we found rather inefficient considering the size of the models. In this article, we introduce a lightweight deep Convolutional Neural Network (dCNN) based method for rice leaf disease detection, that outperforms contemporary state-of-the-art methods and showcases competitive performance against 21 established benchmark architectures, including AlexNet, MobileNet, ResNet50, DenseNet121, ResNeXt50, ShuffleNet, ConvNext, EfficientNet, GogoleNet, SwinTransformer, VisionTransformer, and MaxVit, to name a few, with significantly lower trainable parameters. Notably, our method achieves an accuracy score of 99.81%, a precision score of 0.99828, a recall score of 0.99826, and an f1-score of 0.99827. Moreover, we enhance the rice leaf disease dataset by merging two existing datasets and supplemented them with an additional 95 manually annotated images gathered from publicly available sources on the internet. We also develop a comprehensive crop health monitoring system for farmers, and develop an open API for the automatic annotation of new instances, benefiting the research community at large. INDEX TERMS deep learning; deep convolutional neural network; rice leaf disease detection; leaf disease classification

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“…Venu Vasantha et al, [28] discuss recently introduced methodologies with their performance measure and offer potential solutions using various machine learning techniques and comparative study of algorithms for detecting the sort of disease that afflicted the crop based on the crop image data. According to Asfaqur Rahman et al [29], the suggested model will successfully categorize and identify the diseases affecting rice leaves using image processing methods. CNN was employed to carry out and deliver the 90% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model

Kumar

Jaganathan²,

Viswanathan

et al. 2023

Environ. Res. Commun.

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To ensure a higher quality, capacity and production of rice, it is critical to diagnose disease early in order to decrease the usage of pesticides and reduce agricultural and environmental damage. Therefore, a Multi-scale YOLO v5 detection network is proposed to resolve rice crop disease in its early stage. The experiment initially starts with the rice leaf images from the RLD dataset for pre-processing, after which data set labels are created, which are then divided into train and test sets. DenseNet-201 is used for the backbone network and depth aware instance segmentation is used to segment the different regions of rice leaf. At next, the proposed Bidirectional Feature Attention Pyramid Network (Bi-FAPN) is used for extracting the features from the segmented image and enhances the detection of disease with different scales. The feature maps are finally identified in the detection head, where the anchor boxes are then applied to the output feature maps to produce the final output vectors by the YOLO v5 network. The experiments are conducted with RLD dataset with different existing networks. The effectiveness of the network is evaluated based on various parameters in terms of average precision, accuracy, average recall, IoU and F1 score.

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Rice Disease Detection Based on Image Processing Technique

Cited by 12 publications

References 4 publications

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Towards Sustainable Agriculture: A Novel Approach for Rice Leaf Disease Detection Using dCNN and Enhanced Dataset

Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model

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