Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

Arinichev, Igor V.; Polyanskikh, S. V.; Волкова, Г. В.; Arinicheva, Irina

doi:10.5391/ijfis.2021.21.1.1

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…To detect leaf blight and determine its severity, a segmentation model based on the U-Net architecture was utilized [10,11]. Unlike previous fully convolutional networks where spatial information is lost due to bottlenecks between layers and sharp tensor size increments with resolution increase, U-Net-based models perform upsampling more smoothly.…”

Section: Methodsmentioning

confidence: 99%

Digital monitoring of crops in grain ecosystems

Arinichev,

Arinicheva,

Foshchan

et al. 2023

BIO Web Conf.

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In the conditions of rapid global population growth, resource depletion, and increasing demand for grains, an efficient agricultural management system becomes a crucial element for ensuring food security in Russia and worldwide. The foundation of such management is an intelligent grain production monitoring system, where diagnosing grain crop diseases serves as a critically significant subsystem. This article presents an approach based on the utilization of neural networks, specifically the U-Net architecture for semantic segmentation, adapted for the analysis and detection of helminthosporium through images of maize leaves. Quality evaluation of segmentation employs metrics like Intersection over Union (IoU) and Dice coefficient, computed from a held-out dataset, ensuring an objective assessment of results. The research demonstrates high accuracy and similarity between the model's predictions and expert annotations, while also showcasing the convergence of loss function during neural network training. A notable advantage of the proposed approach lies in the lightweight nature of the suggested architecture and the ability to utilize trained models as cores for decision support systems, including on local devices without network connectivity.

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Section: Methodsmentioning

confidence: 99%

Digital monitoring of crops in grain ecosystems

Arinichev,

Arinicheva,

Foshchan

et al. 2023

BIO Web Conf.

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“…Igor V. Arinichev et al tested the performance of four relatively lightweight classical convolutional neural networks for rice disease identification: GoogleNet, ResNet-18, SqueezeNet-1.0, and DenseNet-121 (Arinichev et al, 2021). AlexNet and VGG networks were usually not considered in lightweight operations due to their complex structure and large number of parameters.…”

Section: Figurementioning

confidence: 99%

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Ning,

Liu,

Zhu

et al. 2023

Front. Plant Sci.

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There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

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“…Note that in previous articles [4][5][6], the authors considered and solved other types of diagnostic tasks related to diseases of cereal crops, including winter barley. Specifically, utilizing computer vision technologies has resulted in high diagnostic accuracy and effectiveness in tasks such as the classification and segmentation of diseases in winter barley.…”

Section: Introductionmentioning

confidence: 99%

Innovative approach to diagnosing the development of net blotch in winter barley

Arinichev,

Arinicheva,

Volkova

2024

BIO Web Conf.

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The article demonstrates the possibility of applying intelligent digital technologies to forecast the development of net blotch disease in winter barley caused by Pyrenophora teres. The obtained intelligent solution in the form of a binary decision tree has the ability to determine scenarios of net blotch development into three classes: depressive, moderate, and epiphytotic. To effectively train the model during the period from 2021 to 2023, field and laboratory experiments were conducted on the research sites of the Federal Research Center of Biological Plant Protection. The experiments collected data on the degree of leaf damage, the type of resistance of the variety, the growth phase at which primary infection occurred, and the average relative air humidity during the vegetation period in which infection occurred. The total sample size was 249 observations. The trained model demonstrated high classification accuracy on both the training and test sets, with an accuracy rate exceeding 0.96 and 0.92, respectively. The study shows that the innovative method discussed in the article for forecasting the development of net blotch in winter barley can be successfully integrated into the overall strategy of phytosanitary diagnostics of grain crops, including barley.

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Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

Cited by 13 publications

References 18 publications

Digital monitoring of crops in grain ecosystems

Digital monitoring of crops in grain ecosystems

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Innovative approach to diagnosing the development of net blotch in winter barley

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