Deep Residual Learning Approach forPlant Disease Recognition

Pavel, Monirul Islam; Rumi, Roisul Islam; Fairooz, Fabiha; Jahan, Sigma; Hossain, Mohammad Amzad

doi:10.1007/978-3-030-49795-8_49

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…However, this technique failed to forecast the accurate value. Pavel et al, 20 devised a deep learning-based system with an IoT model for recognizing the disease in plants. Here, the plant village data set was considered for analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep computation model to the estimation of sulphur dioxide for plant health monitoring in IoT

Karnati¹,

Rao

Prakash

et al. 2021

Int J Intell Syst

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The Internet of Things (IoT) is an emerging domain in recent days as they provided a huge number of applications in day-to-day lives. In contrast to the agricultural sector, the automatic techniques for recognizing plant disease have different benefits and pose several issues. In addition, inappropriate diagnoses are ineffectual in treating the disease and may affect the crop yield. This paper presents a novel technique for plant health monitoring by estimating sulphur dioxide. Here, the simulation of IoT was performed for improved functioning. After that, the cluster head selection and routing are performed using the proposed invasive water cycle (IWC) algorithm, which is devised by integrating the water cycle algorithm (WCA) and invasive weed optimization (IWO) algorithm. Here, the fitness function is newly modeled using certain factors involving Energy, intra and intercluster distance, and delay. After the cluster head selection and routing, the sulphur dioxide content from the soil is estimated. For sulphur dioxide estimation, the soil data is considered the input data, and then the data transformation is performed to transform the data. After that, the feature selection is performed by Mahalanobis distance, and then sulphur dioxide from the soil is estimated using Deep Q-Network, where training is performed using the proposed IWC algorithm. The proposed IWC-based Deep Q-Network offered improved performance with the highest accuracy of 0.941, and the smallest root mean square error (RMSE) of 0.242. In addition, the minimal Energy and highest Throughput are computed by the proposed IWC-based Deep Q-Network.

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Section: Literature Reviewmentioning

confidence: 99%

Deep computation model to the estimation of sulphur dioxide for plant health monitoring in IoT

Karnati¹,

Rao

Prakash

et al. 2021

Int J Intell Syst

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“…Other fields, such as Biology, are also using these type of approaches. Pavel et al [19] use ResNet34 to identify diseases in plants from images of their leaves. The dataset is composed of 7600 images (200 images for each category).…”

Section: Introductionmentioning

confidence: 99%

An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates: Experimental Validation

et al. 2023

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Image-based methods have been applied to support structural monitoring, product and material testing, and quality control. Lately, deep learning for compute vision is the trend, requiring large and labelled datasets for training and validation, which is often difficult to obtain. The use of synthetic datasets is often applying for data augmentation in different fields. An architecture based on computer vision was proposed to measure strain during prestressing in CFRP laminates. The contact-free architecture was fed by synthetic image datasets and benchmarked for machine learning and deep learning algorithms. The use of these data for monitoring real applications will contribute towards spreading the new monitoring approach, increasing the quality control of the material and application procedure, as well as structural safety. In this paper, the best architecture was validated during experimental tests, to evaluate the performance in real applications from pre-trained synthetic data. The results demonstrate that the architecture implemented enables estimating intermediate strain values, i.e., within the range of training dataset values, but it does not allow for estimating strain values outside those range. The architecture allowed for estimating the strain in real images with an error ∼0.5%, higher than that obtained with synthetic images. Finally, it was not possible to estimate the strain in real cases from the training performed with the synthetic dataset.

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“…Themotherofallcivilizationisagriculture (Jasim&AL-Tuwaijari,2020).Agriculturehasbeen practicedsinceancienttimes,andmanyinnovationsanddevelopmentsaretakingplace (Pooja,Das, &Kanchana,2017).Themostcriticaldrivingforceforacountry'seconomyisagriculture,whichis thelivelihoodofnearlytwothirdsofthepopulationofadevelopingcountry (Hossain,Hossain,& Rahaman,2019).Cocoaisacashcropusedformanufacturingvariousproductssuchaschocolate drinks,bars,cocoabutter,cocoapowder,andamongothers.Despiteallitsnumerousbenefits,cocoa farmersfacemanychallengesinitsproduction.Cocoaisatahighriskofbeingattackedbypestsand diseasesinthehotandhumidenvironment.Almosthalfoftheproductioncouldbelostduetopest infestation,diseases,andweed.Diseasesthatcanaffectyields-mostcommoninWestAfricaarethe blackpodandtheswollenshootvirus (Ameyaw,Dzahini-Obiatey,&Domfeh,2014).Plantdiseases havecontributedtoamajordecreaseinworldwidecropproductionandquantity (Sun,Zhang,Yang, &Liu,2020).Thishasresultedinadrasticreductionintheproductionandquantityofcropsacross theworld.Ifthesediseasescanbepreciselydetectedandtreatedontime,theeconomiclosseswill besignificantlyminimized,andecologicaldisasterscausedbydiseasetransmissioncanbeprevented (Sun,Zhang,Yang,&Liu,2020).Asignificantthreatisdiseaseinfestationincropsthatcannegatively impactanation'seconomy,includingitsproductionandyields,ifleftunattendedto (Francis&Deisy, 2019).Agricultureisoneofthemostimportantsourcesoflivelihoodthatcontributesgreatlytothe economyoftheworld.However,toagreatextent,plantdiseasescausedbyvariouspathogenscan curb yield (Pavel, Rumi, Fairooz, Jahan, & Hossain, 2021). Improving the agricultural product's quality has become critical (Machha, Jadhav, Kasar, & Chandak, 2020).…”

Section: Introductionmentioning

confidence: 99%

Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection

Appati

2021

International Journal of Technology Diffusion

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An accurate and reliable cocoa swollen shoot disease diagnosis is the desire of traditional farmers with low-resolution smart devices. In this study, an efficient cocoa swollen shoot disease identification method base on transfer learners using pre-trained VGG16 and ResNet was proposed. These pre-trained models were trained using 456 samples and validated with 114 samples. The dataset constitutes low-resolution images, VGG16 and ResNet, and achieved an accuracy of 98.25 and 94.73%, respectively. With the objective of proposing a more reliable and accurate model, VGG16 is noted to scale better in terms of performance for implementation.

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Deep Residual Learning Approach forPlant Disease Recognition

Cited by 6 publications

References 13 publications

Deep computation model to the estimation of sulphur dioxide for plant health monitoring in IoT

Deep computation model to the estimation of sulphur dioxide for plant health monitoring in IoT

An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates: Experimental Validation

Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection

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