Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

Taha, Mohamed Farag; Abdalla, Alwaseela; ElMasry, Gamal; Gouda, Mostafa; Zhou, Lei; Zhao, Nan; Liang, Ning; Niu, Zhiyong; Hassanein, Amro; Al‐Rejaie, Salim S.; He, Yong; Qiu, Zhengjun

doi:10.3390/chemosensors10020045

Cited by 23 publications

(14 citation statements)

References 66 publications

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“…More recently, Taha et al used a CNN (ResNet18 and Inceptionv3) to diagnose the nutrient deficiencies of lettuce grown in aquaponics. The results demonstrated that the proposed deep model (Inceptionv3) obtained an accuracy of 96.5 % [69]. Table 5 summarizes the results and outcomes obtained from these research endeavors in terms of the prediction of water quality parameters, detection and species classification, estimation of fish size, feeding decisions of fish, and plant detection using deep learning.…”

Section: Plant Detectionmentioning

confidence: 93%

“…Deep learning models have achieved remarkable success in many agricultural applications such as detecting and diagnosing plant disorders [69], predicting plant water content [70], and identifying plant species [71]. In addition to the contributions of deep learning in the field of aquaculture, such as fish detection and classification [72], estimating the age and size of fish [73], behavior analysis [74], and feeding decisions [75], there are dozens of other potential applications of this approach in smart aquaponics systems.…”

Section: Neural Network and Deep Learning Methods For Smart Aquaponicsmentioning

confidence: 99%

“…Plant disease detection CNN 99.35% [91] Diagnose nutrient deficiencies of lettuce ResNet18 and Inceptionv3 96.5% [69] Monitor the growth rate of lettuce Mask R-CNN 97.63% [92] Prediction tomato yield and stem growth RNN with LSTM Performed well [93] 5.3. Aquaponics and Industry 4.0 Industry 4.0 is an initiative that integrates many emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data and analytics (BDA), cyberphysical systems (CPS), wireless sensor networks (WSN), autonomous robot systems (ARS), interconnectivity, automation, machine learning, real-time data acquisition, and cloud computing [94,95].…”

Section: 08% [88]mentioning

confidence: 99%

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Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview

et al. 2022

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Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective.

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Section: Plant Detectionmentioning

confidence: 93%

Section: Neural Network and Deep Learning Methods For Smart Aquaponicsmentioning

confidence: 99%

Section: 08% [88]mentioning

confidence: 99%

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Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview

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“…Figure 3 also shows the optimization design of the convolution process for the FPN network structure. Since the original Mask R-CNN algorithm uses the ResNet residual network as the skeleton network, which has good performance, is easy to train, and can be stacked with many layers, ResNet is used as the basic network, and the FPN idea is added for illustration [21]. Its calculation is as follows: k=k0+log2(wh/224),where k represents the feature graph, and w and h represent the width and height of the feature graph, respectively.…”

Section: Design Of the Mask Region-based Convolutional Neural Network...mentioning

confidence: 99%

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model

Chen²,

2023

Phil. Trans. R. Soc. A.

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The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

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“…A graph theoretic approach has been used by ( Bashyam et al., 2021 ) to detect and track individual leaves of a maize plant for automated growth stage monitoring. The method by ( Azimi et al., 2021 ) uses Convolutional Neural Network - Long Short Term Memory (CNN-LSTM) for water stress classification in chickpea plants, whereas the method by ( Taha et al., 2022 ) uses deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. In this paper, we present a novel algorithm based on convolutional neural networks to determine the qualitative and quantitative propagation of drought stress in cotton plants by classifying reflectance spectra generated from hyperspectral image sequences.…”

Section: Introductionmentioning

confidence: 99%

Drought stress prediction and propagation using time series modeling on multimodal plant image sequences

Das

Saha²,

Samal³

et al. 2023

Front. Plant Sci.

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The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.

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Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

Cited by 23 publications

References 66 publications

Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview

Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model

Drought stress prediction and propagation using time series modeling on multimodal plant image sequences

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