Research article

Feature fusion based artificial neural network model for disease detection of bean leaves

  • Received: 19 December 2022 Revised: 16 February 2023 Accepted: 23 February 2023 Published: 28 February 2023
  • Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.

    Citation: Eray Önler. Feature fusion based artificial neural network model for disease detection of bean leaves[J]. Electronic Research Archive, 2023, 31(5): 2409-2427. doi: 10.3934/era.2023122

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  • Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.



    Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease secondary to repetitive mild traumatic brain injury (mTBI), including concussions and sub-concussive impacts, resulting in long-term issues with cognition, behavior and mood [1][7]. CTE was initially recognized in boxers who developed symptoms like ataxia, memory loss and personality change, and it was coined as the “punch drunk” syndrome or “dementia pugilistica” [1],[3],[4],[8][10]. Over time, it became evident that CTE also affected military personnel, domestic violence victims and those participating in contact sports like football, ice hockey, professional wrestling, rugby, soccer and boxing [1][4],[8].

    Neurodegeneration and symptoms in CTE progress even in the absence of further traumatic insults [2],[6],[11],[12]. mTBI is thought to trigger an inflammatory cascade and lead to blood brain barrier permeability, axonal injury and micro-hemorrhages [13][16]. As a result, there is deposition of pathogenic proteins, including the pathogenic cis-isoform of p-tau, which, through the process termed cistauosis, catalyzes conversion of normal into pathogenic tau [2],[17][21]. As such, CTE develops in pathological stages with worsening depositions of p-tau, neurofibrillary tangles and brain atrophy in similar but distinct fashions as other neurodegenerative diseases like Alzheimer's disease [2].

    The current gold standard diagnosis for CTE is post-mortem pathological examination. Trauma encephalopathy syndrome was proposed to help diagnose patients with CTE. This criterion consists of a history of repetitive brain injury, persistent symptoms over a year and an absence of comorbidities that may also account for the symptoms. Also present should be a cognitive, behavioral or mood impairment in the presence of progressive decline over more than a year, impulsivity or headaches [2],[22][24]. However, patients with CTE present differently and there is no consensus on a single, best set of clinical or research diagnostic criteria [2],[5],[6],[25]. Therefore, there is increasing investigation of adjunctive non-invasive diagnostic modalities. In this paper, we review the recent advances in the use of neuroimaging and fluid biomarkers for early CTE detection.

    Magnetic resonance imaging (MRI) produces images by analyzing tissue characteristics using magnetic fields and radio waves. It is the current imaging modality of choice due to its improved soft tissue differentiation, ability to detect diffuse axonal injury and lack of ionizing radiation compared to computed tomography (CT). The gross, macroscopic structural changes with CTE include cerebral atrophy that is most severe in the frontotemporal lobes, vermis, thalamus, mamillary bodies and hypothalamus. There is also ventricular enlargement, thinning of the corpus callosum and depigmentation of the substantia nigra and locus coeruleus. Though it is not a consistent feature of CTE, neuropathologic change (CTE-NC), i.e., the presence of cavum septum pellucidum in imaging, is associated with CTE. Microhemorrhages representing diffuse axonal injury may also be present [26][30]. These structural findings are not specific to CTE, however [30]. Therefore, there has been increasing research on the use of alternative, more advanced imaging methods as tools to identify and understand the progression of CTE in vivo.

    Methods of quantitative brain volume analyses, like assessing cortical thickness in other neurodegenerative diseases, have provided useful for diagnosis and prognosis [31][34]. A study showed that hippocampal volume in football athletes was inversely correlated with the presence of concussions and amount of football played [35]. These findings, however, may not be specific to CTE considering the volume loss seen in other neurodegenerative diseases [36]. Furthermore, the inclusion criteria consisted of sport participation within the last year in those aged between 18–26. Therefore, it did not necessarily include or correlate with the presence of neuropsychiatric symptoms associated with CTE [16].

    Diffusion tensor imaging (DTI) is an MRI technique that examines the longitudinal diffusion of water through axons to evaluate the orientation and integrity of white matter tracts. A fractional anisotropy (FA) close to 1 means that diffusion occurs along one axis and is otherwise restricted. Axial diffusivity (AD) and radial diffusivity (RD) are similar measures that reflect the magnitude of diffusion running parallel and perpendicular to white matter tracts, respectively [37],[38]. As such, decreased FA, decreased AD and increased RA would be expected in CTE due to decreased white matter integrity. These findings have been demonstrated in mTBI, and even had prognostic value [39][47]. A post-mortem tissue DTI analysis of patients with confirmed CTE-NC by Holleran et al. demonstrated associations between decreased FA and reduced white matter integrity [38]. A DTI analysis by Herweh et al. that evaluated male amateur boxers demonstrated associations between decreased FA and neuropsychological outcomes [48]. A study by Kraus et al. showed that study subjects who were included on the basis of having a history of mTBI (22 subjects) or moderate to severe TBI (17 subjects) had decreased FA and RD. The study also suggested that DTI can help to determine the relationship between TBI and cognitive differences and distinguish the spectrum and severity of TBI [49]. Another study showed decreased FA and no changes in the RD and FA in patients who were football players with sub-concussive impacts, with return to baseline after they abstained from play. Further studies are needed to assess the utility of RD and AD in patients specifically with CTE [50].

    Functional MRI (fMRI) is also known as blood oxygen level-dependent MRI. Neuronal activation in specific brain areas results in an increased oxyhemoglobin-to-deoxyhemoglobin ratio secondary to increased local blood flow, resulting in changes in magnetic susceptibility that are detected by fMRI when a specific task is performed [51]. This method is heavily used in behavioral and physiologic research, as it correlates well with neuronal activity. A theoretical limitation is that the results may be confounded in patients with CTE who already have reduced and altered cerebral blood flow. This modality is yet to be investigated in CTE. A few studies have, however, demonstrated altered brain activation patterns in the fMRI results of living patients with acute and repetitive mTBI [52][62]. The correlation between fMRI and altered brain activation patterns in those with mTBI may overwhelm the theoretical limitation. Furthermore, arterial spin imaging MRI, a type of fMRI, has shown to represent aberrant cerebral blood flow in those with mTBI [63].

    Magnetic resonance spectroscopy measures concentrations of metabolites within the brain based on the chemical shift of their protons, which is determined by the proton's chemical environment. This modality is useful for CTE when considering the pathological changes, including neuroinflammation, in the acute and chronic stages of the disease. In fact, a study investigating male USA National Football League (NFL) players between 40–69 with self-reported neuropsychiatric symptoms were found to have decreased cellular energy metabolism, as evidenced by lower creatinine in the parietal white matter. Neuro-inflammatory metabolites like glutamate, glutathione and myo-inositol also correlated with their behavioral and mood symptoms [64]. Several other studies have demonstrated metabolite abnormalities in patients with a history of repeated head impacts, including decreases in NAA, NAA/Cho and NAA/Cr, as well as increases in Cho, ml, glutamine, choline, fucose and phenylalanine [65][70].

    Susceptibility weighted imaging (SWI) takes advantage of different responses, or susceptibilities, to molecules within a magnetic field. These susceptibilities are measured as phase shifts and superimposed on an MRI, highlighting local susceptibility changes. In the setting of TBI, it can be used to reveal hemorrhagic contusions or diffuse axonal injury. SWI abnormalities, including microhemorrhages, have been demonstrated in contact sport participants, active duty military members and those with concussive-like symptoms and a history of repetitive mTBI [71][74]. Considering that it has shown utility in predicting neuropsychiatric outcomes for those with acute mTBI, its use should be considered in predicting the likelihood of the development of CTE [75],[76]. Neurodegenerative disorders often have characteristic features and positions of cerebral microbleeds [77],[78]. Research investigating the distribution and features of cerebral microbleeds in CTE to make a specific diagnosis would be beneficial.

    Position emission tomography (PET) CT, which employs the use of radioisotopic biomarkers, has been garnering interest for elucidating elevated tau, beta-amyloid, neurofibrillary tangles and other neuroinflammatory proteins [79]. For example, FDDNP binds to the neurofibrillary tangles and proteins that are associated with CTE. As such, it has been employed in the diagnosis of CTE [79],[80]. However, FDDNP has also been shown to bind to beta-amyloid and hyperphosphorylated tau and is therefore limited in specificity when discriminating against other neurological degenerative diseases, such as Alzheimer's disease [79].

    The development of biomarkers specific for the hyperphosphorylated tau proteins associated with CTE, such as [18F]AV-1451 (flortaucipir), are of interest and have been recently studied. [18F]AV-1451 binds to the paired helical tau deposition associated with Alzheimer's disease, and studies are being conducted to investigate its utility for visualizing tau deposition patterns that are associated with CTE, such as those in the medial temporal lobe, brain stem and diencephalon [81]. One such study involved [18F]AV-1451-PET scans from 26 former NFL players aged between 40–69 with reported neuropsychiatric symptoms; the researchers observed a statistically significant increase in the mean standard reuptake of [18F]AV-1451 in the bilateral superior frontal, bilateral medial temporal and left parietal regions as compared to the controls [82]. Another study followed a retired NFL player who underwent an MRI and [18F]AV-1451 PET scan, which revealed uptake in the bilateral medial temporal lobes and parietal regions 4 years before a post-mortem diagnosis of stage-4 CTE [83]. These studies, which are confined to small sample sizes and the single case report, illustrate the need for further investigation to validate [18F]AV-1451 as an optimal radiotracer for in vivo PET scans to diagnose CTE. Other developing PET tracers of interest that bind to tau proteins include [11C]PBB3[84], THK-5105[85], THK-5117[86], THK-5351[87] and T807[88]. [18F]florbetapir and [11C]PiB PET measure amyloid beta plaques [89],[90]. The characteristics of selected biomarkers are reviewed in Table 1. Studies have also investigated the use of [11C]flumazenil and [18F]flumazenil, which bind to the GABAA system in patients with a history of repeated head injury [91]. Lastly, the translocator protein (TSPO) and copper have also been targeted with several radiotracers to assess inflammation [91]. Several studies have also investigated the use of [18F]FDG for patients with a history of mTBI, generally showing decreased brain glucose metabolism in the cerebellum, vermis, pons, temporal lobe, prefrontal cortex and limbic system [91]. Though cortical uptake regions vary, studies have generally consistently demonstrated uptake in the temporal lobes, limbic system, midbrain, hippocampi and amygdalae [2],[79],[91]. The need for further research into PET biomarkers for hyperphosphorylated tau proteins specifically associated with CTE and tasked with reducing off-target binding is warranted.

    Table 1.  PET CT biomarkers.
    Biomarker Specificity
    FDDNP Affinity for intracellular neurofibrillary tangles but has been found to be “non-selective” due to its binding with extracellular β-amyloid and tau, which is a feature of Alzheimer's disease and not necessarily CTE [17],[79],[91]
    [18F]AV-1451 (flortaucipir) Affinity for hyperphosphorylated tau proteins. However, there is a possibility of false negatives, as there is also high binding affinity for paired helical tau filaments in Alzheimer's disease and not CTE [91],[92].
    [11C]PBB3 Affinity for Alzheimer's disease tau pathology but has mixed reviews over its ability to identify paired helical tau filaments in CTE [93],[94]
    THK-5105 High binding affinity to tau protein aggregates and tau-rich Alzheimer disease, but it has reported to have a high background signal in PET images, which could affect its utility. Also, it has not been investigated for tau proteins seen in CTE-related pathology [86],[95].
    THK-5117 Affinity for Alzheimer's disease-related tau protein in the medial temporal lobe in port-mortem patients, but not yet investigated for the CTE-related tau deposits [96].
    THK-5351 Showed affinity with increased t-tau levels in the parahippocampal gyrus, but not investigated for CTE-associated tau patterns to date [97].

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    CTE is characterized by an accumulation of differentiated cis p-tau proteins in the vasculature of sulcal depths with large groups of astrocytic tangles, neurofibrillary tangles and neurites [17]. These abnormal, non-functional p-tau clusters develop within axons [17],[98]. High values of p-tau are also found in single-event TBI and other neurodegenerative diseases, and therefore cannot serve as the sole means of diagnosis of CTE [99],[100]. However, it can serve as a biomarker when considered in the overall clinical setting with accompanying imaging findings from developing diagnostic tools, like PET.

    A promising biomarker that is not as widely employed for tau is the inflammatory marker triggering receptor expressed on myeloid cells 2 (TREM2), a triggering receptor found in several myeloid lineage cells such as peripheral macrophages, dendritic cells and microglial cells in the central nervous system (CNS) [101]. TREM2 is also expressed in the microglia of the brain, regulating microglial activation and playing a multi-faceted role in its immune response [102],[103]. Animal studies have shown that TREM2 is upregulated in the early stages following injury, making it a potential biomarker for TBI and other head injuries [104]. When microglia in the brain are activated, following injury, cleavage of TREM2 by proteases follows. These proteases release soluble TREM2 (sTREM2), indicating that injury has occurred. A study highlighted the relationship between sTREM2 levels in cerebrospinal fluid (CSF) and t-tau concentrations in 68 former NFL players aged 40–69 with self-reported neuropsychiatric symptoms (compared to healthy controls), ultimately finding a positive correlation between the two [102]. The presence of TREM2 variants increases the likelihood of developing Alzheimer's disease by 2–4 times [105],[106]. Therefore, elevated levels of sTREM2 are non-specific. Because upregulation of TREM2 begins early and persists over time [104], it could prove to be a key inflammatory marker used for the diagnosis of CTE in the appropriate clinical context.

    A biomarker with potential for pre-mortem CTE diagnosis is the chemokine C-C motif chemokine ligand 11 (CCL11). Chemokines are proteins that play a central role and facilitate biochemical and cellular events in the immune response. They upregulate leukocytes and act as secondary pro-inflammatory mediators [107],[108]. CCL11 is a chemoattractant of eosinophils in the peripheral immune system and has recently been shown to also penetrate the blood brain barrier [107]. A study showed that the brain of mice secreted CCL11 as a response to the inflammation of astrocytes, pericytes and microglia [109]. A key study showed an increase in the plasma blood levels of CCL11 to correlate with a decrease in learning, memory and neurogenesis in the brains of mice [110]. It has been proposed that the main reason for CCL11's involvement in neurological decline is its ability to increase the microglial production of reactive oxygen species and promote excitotoxic neuronal death [111]. A study indicated that CCL11 is released in the CSF from the choroid plexus in the brain, suggesting direct effects on the brain [112]. This is also associated with an increase in the ratio of cytokine interleukin (IL)-4 and interferon (IFN)-γ in the choroid plexus and CSF. Prior research has shown the physiological importance of CCL11 to neurological function, but it may be a useful biomarker for CTE considering its ability to distinguish it among other neurodegenerative diseases. A collection of studies showed that plasma CCL11 increased in patients with Alzheimer's disease and Huntington's disease, while it decreased in amyotrophic lateral sclerosis and secondary progressive multiple sclerosis [113][115]. Using ELISA, a post-mortem study of 23 former football players with neuropathologically diagnosed CTE and 50 subjects with neuropathologically diagnosed Alzheimer's disease showed a statistically significant increase in the CCL11 levels in the dorsolateral frontal cortex of CTE subjects compared to the Alzheimer's disease and control subjects [116]. Another study with subjects aged 25–33 showed significant increases in CCL11 in the CSF of retired football players relative to swimmers with no TBI history and a sedentary control group. Analysis of the IL-4-to-IFN-γ ratio also showed significant increase in the football players compared to the others in the study. Lastly, CCL11 levels showed a strong positive correlation with the number of years of football played [107]. There is a lot of promise that CCL11 can provide CTE diagnosis for patients pre-mortem. More comprehensive research can be done to analyze the relationship of CCL11 levels with the IL-4-to-IFN-γ ratio found in the CSF of different types of neurodegenerative diseases in pre- and post-mortem brains to build a more accurate predictive model for diagnosis.

    Another biomarker widely employed in TBI is neurofilament-L (NfL). NfL comes from the intermediate filament protein family and is part of the neuronal cytoskeleton [117]. It can serve as an indicator of CNS axonal damage. NfL is released into the CSF [117]. A meta-analysis with a sample size of 1118 patients showed that NfL CSF, serum and plasma levels were significantly higher in patients with TBI compared to the control patients without prior TBI [117]. Another study showed that patients with Alzheimer's disease, Guillain-Barré-syndrome and amyotrophic lateral sclerosis had increased levels of serum NfL compared to a control without CNS damage [118]. These studies suggest that NfL could be used to also conduct future research toward CTE diagnosis.

    Glial fibrillary acidic protein (GFAP) is a cytoskeletal monomeric filament protein located in the astroglial cells of gray and white matter [119]. A study showed that levels of GFAP, tau and NfL were all higher in the group of 277 patients suspected with mTBI, with GFAP yielding high discriminatory power in differentiating these patients from the 49 healthy controls, with an area under the curve (AUC) of 0.93 [120]. In the same study, GFAP similarly served as a strong predictor of mTBI when examining MRI abnormalities, with an AUC of 0.83 [120]. Another study of GFAP's ability to predict CT abnormalities showed an AUC of 0.88 when examining 215 patients (83% with mTBI; mean age 42.5 ± 18.0) [121]. Further analysis could be done using pre-mortem CTE subjects and comparing GFAP levels in those patients to a control to assess whether this translates to specifically diagnosing CTE over other diseases as a result of a TBI. The characteristics of the biomarkers are summarized in Table 2.

    Table 2.  Blood biomarkers for the diagnosis of CTE.
    Biomarker Description Significance Advantages Limitations
    p-tau Hyperphosphorylated tau protein found in the cortical vasculature within the sulcal depths [17]. -Repetitive head injury causes the conversion of typical tau protein to p-tau [2].
    -P-tau is indicative of axonal functional decline and will deposit in predictable patterns and high concentrations following brain injury [17],[122],[123].
    -Consistent and sensitive results considering large concentrations following injury [123].
    -A blood sample is less invasive than lumbar puncture [124].
    Other neurodegenerative diseases also express high concentrations of p-tau [123].
    TREM2 Triggering receptor found in myeloid lineage cells that regulates CNS microglial activation [101]. -Cleavage of TREM2 by proteases following head injury produces sTREM2
    -Increased sTREM2 levels in the CSF are indicative of increased protease activity, likely due to trauma [102].
    Early upregulation of TREM2 following head injury may eventually lead to its diagnostic use in potential CTE cases in vivo [102],[104]. -Invasive sample collection with lumbar puncture [102].
    -TREM2 variants can impair the function of receptors due to poor signaling, ultimately leading to decreased sTREM levels [125].
    CCL11 -Chemokine that serves as a mediator in inflammatory cascades [107],[108].
    -Penetrates the blood brain barrier [107].
    -Secreted into CSF by choroid plexus in the brain [112].
    -Increases microglial production of reactive oxygen species and promotes excitotoxic neuronal death [111].
    -Reflective of neuroinflammatory processes [111].
    -Potential ability to differentiate between CTE and other neurodegenerative diseases [116].
    -Possible correlation with number of repeated head impacts [107].
    -Its main role in the CNS is unclear, as it is a chemoattractant of eosinophils in the peripheral immune system [112].
    NfL Part of the Intermediate filament protein family and of the neuronal cytoskeleton [117]. -Can be measured, as axonal damage induces its release into the CSF [117]. -Released in a delayed fashion and may be correlated with cognitive decline in patients with chronic TBI [126].
    -Specific to CTE [116].
    -Conflicting data regarding its validity in accurately diagnosing CTE [117].
    GFAP Cytoskeletal monomeric filament protein in the brain's astroglial cells [119]. -Reflective of astroglial injury and released acutely following TBI [116]. -Better predictor of mTBI than NfL and p-tau [120]. -Utility in CTE specifically unknown [120],[121].

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    In conclusion, the current clinical diagnosis of CTE relies on clinical symptomatology and structural imaging findings. While there is extensive research on imaging and fluid biomarkers in relation to TBI, there is comparatively limited research on CTE. Particularly, the referenced studies are often small and investigate mTBI, TBI or repetitive head injury and do not study CTE directly. Investigating CTE directly is especially difficult considering the lack of consensus on pre-mortem diagnostic criteria. The studies also frequently include patients based on a history of sport participation alone, self-reported history of TBI or self-reported neuropsychiatric symptoms. The studied biomarkers are also often elevated in other neurodegenerative disorders, and there is relatively limited research on the use of biomarkers to differentiate CTE from other neurodegenerative disorders. Lastly, many of the fluid biomarkers are also elevated following a single TBI event, and there is not an imaging or fluid biomarker approved solely for CTE. We look forward to further research on the early promising imaging modalities and fluid biomarkers to potentially assist in the diagnosis of CTE and in differentiation of it from other neurodegenerative diseases.



    [1] G. S. Malhi, M. Kaur, P. Kaushik, Impact of climate change on agriculture and its mitigation strategies: A review, Sustainability, 13 (2021), 1318. https://doi.org/10.3390/su13031318 doi: 10.3390/su13031318
    [2] K. Yin, J. L. Qiu, Genome editing for plant disease resistance: applications and perspectives, Phil. Trans. R. Soc. B, 374 (2019), 20180322. https://doi.org/10.1098/rstb.2018.0322 doi: 10.1098/rstb.2018.0322
    [3] Z. Hu, What socio-economic and political factors lead to global pesticide dependence? A critical review from a social science perspective, Int. J. Environ. Res. Public Health, 17 (2020), 8119. https://doi.org/10.3390/ijerph17218119 doi: 10.3390/ijerph17218119
    [4] S. Roy, J. Halder, N. Singh, A. B. Rai, R. N. Prasad, B. Singh, Do vegetable growers really follow the scientific plant protection measures? An empirical study from eastern Uttar Pradesh and Bihar, Ind. J. Agric. Sci., 87 (2017), 1668–1672.
    [5] M. Ş. Şengül Demirak, E. Canpolat, Plant-based bioinsecticides for mosquito control: impact on insecticide resistance and disease transmission, Insects, 13 (2022), 162. https://doi.org/10.3390/insects13020162 doi: 10.3390/insects13020162
    [6] W. Cramer, J. Guiot, M. Fader, J. Garrabou, J. P. Gattuso, A. Iglesias, et al., Climate change and interconnected risks to sustainable development in the Mediterranean, Nat. Clim. Change, 8 (2018), 972–980. https://doi.org/10.1038/s41558-018-0299-2 doi: 10.1038/s41558-018-0299-2
    [7] H. N. Fones, D. P. Bebber, T. M. Chaloner, W. T. Kay, G. Steinberg, S. J. Gurr, Threats to global food security from emerging fungal and oomycete crop pathogens, Nat. Food, 1 (2020), 332–342. https://doi.org/10.1038/s43016-020-0075-0 doi: 10.1038/s43016-020-0075-0
    [8] M. Tudi, H. D. Ruan, L. Wang, J. Lyu, R. Sadler, D. Connell, et al., Agriculture development, pesticide application and its impact on the environment, Int. J. Environ. Res. Public Health, 18 (2021), 1112. https://doi.org/10.3390/ijerph18031112 doi: 10.3390/ijerph18031112
    [9] A. S. Tulshan, N. Raul, Plant leaf disease detection using machine learning, in 2019 10th International Conference on Computing, Communicatıon and Networkıng Technologıes (ICCCNT), 2019. https://doi.org/10.1109/ICCCNT45670.2019.8944556
    [10] A. Kumar, J. P. Singh, A. K. Singh, Randomized convolutional neural network architecture for eyewitness tweet identification during disaster, J. Grid Comput., 20 (2022). https://doi.org/10.1007/s10723-022-09609-y doi: 10.1007/s10723-022-09609-y
    [11] L. Xu, J. Xie, F. Cai, J. Wu, Spectral classification based on deep learning algorithms, Electronics, 10 (2021), 1892. https://doi.org/10.3390/electronics10161892 doi: 10.3390/electronics10161892
    [12] Ü. Atila, M. Uçar, K. Akyol, E. Uçar, Plant leaf disease classification using Efficient Net deep learning model, Ecol. Inf., 61 (2021), 101182. https://doi.org/10.1016/j.ecoinf.2020.101182 doi: 10.1016/j.ecoinf.2020.101182
    [13] S. Zhang, S. Zhang, C. Zhang, X. Wang, Y. Shi, Cucumber leaf disease identification with global pooling dilated convolutional neural network, Comput. Electron. Agric., 162 (2019), 422–430. https://doi.org/10.1016/j.compag.2019.03.012 doi: 10.1016/j.compag.2019.03.012
    [14] D. Jakubovitz, R. Giryes, M. R. Rodrigues, Generalization error in deep learning, in Compressed Sensing and Its Applications: Third International MATHEON Conference 2017, Birkhäuser, Cham, (2019), 153–193. https://doi.org/10.48550/arXiv.1808.01174
    [15] A. Al-Saffar, A. Bialkowski, M. Baktashmotlagh, A. Trakic, L. Guo, A. Abbosh, Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networks, IEEE Trans. Comput. Imaging, 7 (2020), 13–21. https://doi.org/10.1109/tci.2020.3041092 doi: 10.1109/tci.2020.3041092
    [16] G. Algan, I. Ulusoy, Image classification with deep learning in the presence of noisy labels: A survey, Knowl.-Based Syst., 215 (2021), 106771. https://doi.org/10.1016/j.knosys.2021.106771 doi: 10.1016/j.knosys.2021.106771
    [17] C. Wu, S. Guo, Y. Hong, B. Xiao, Y. Wu, Q. Zhang, Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks, Quant. Imaging Med. Surg., 8 (2018), 992. https://doi.org/10.21037/qims.2018.10.17 doi: 10.21037/qims.2018.10.17
    [18] K. Aderghal, A. Khvostikov, A. Krylov, J. Benois-Pineau, K. Afdel, G. Catheline, Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning, in 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), IEEE, (2018), 345–350. https://doi.org/10.1109/cbms.2018.00067
    [19] D. Chen, Y. Lu, Z. Li, S. Young, Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems, Comput. Electron. Agric., 198 (2022), 107091. https://doi.org/10.1016/j.compag.2022.107091 doi: 10.1016/j.compag.2022.107091
    [20] M. Ahsan, M. A. Based, J. Haider, M. Kowalski, COVID-19 detection from chest X-ray images using feature fusion and deep learning, Sensors, 21 (2021), 1480. https://doi.org/10.3390/s21041480 doi: 10.3390/s21041480
    [21] L. Wei, K. Wang, Q. Lu, Y. Liang, H. Li, Z. Wang, et al., Crops fine classification in airborne hyperspectral imagery based on multi-feature fusion and deep learning, Remote Sens., 13 (2021), 2917. https://doi.org/10.3390/rs13152917 doi: 10.3390/rs13152917
    [22] C. Shang, F. Wu, M. Wang, Q. Gao, Cattle behavior recognition based on feature fusion under a dual attention mechanism, J. Visual Commun. Image Represent., 85 (2022), 103524. https://doi.org/10.1016/j.jvcir.2022.103524 doi: 10.1016/j.jvcir.2022.103524
    [23] H. C. Chen, A. M. Widodo, A. Wisnujati, M. Rahaman, J. C. W. Lin, L. Chen, et al., AlexNet convolutional neural network for disease detection and classification of tomato leaf, Electronics, 11 (2022), 951. https://doi.org/10.3390/electronics11060951 doi: 10.3390/electronics11060951
    [24] X. Fan, P. Luo, Y. Mu, R. Zhou, T. Tjahjadi, Y. Ren, Leaf image based plant disease identification using transfer learning and feature fusion, Comput. Electron. Agric., 196 (2022), 106892. https://doi.org/10.1016/j.compag.2022.106892 doi: 10.1016/j.compag.2022.106892
    [25] E. Elfatimi, R. Eryigit, L. Elfatimi, Beans leaf diseases classification using mobilenet models, IEEE Access, 10 (2022), 9471–9482. https://doi.org/10.1109/ACCESS.2022.3142817 doi: 10.1109/ACCESS.2022.3142817
    [26] S. S. Harakannanavar, J. M. Rudagi, V. I. Puranikmath, A. Siddiqua, R. Pramodhini, Plant leaf disease detection using computer vision and machine learning algorithms, Global Transitions Proc., 3 (2022), 305–310. https://doi.org/10.1016/j.gltp.2022.03.016 doi: 10.1016/j.gltp.2022.03.016
    [27] J. Annrose, N. Rufus, C. R. Rex, D. G. Immanuel, A cloud-based platform for soybean plant disease classification using archimedes optimization based hybrid deep learning model, Wireless Pers. Commun., 122 (2022), 2995–3017. https://doi.org/10.1007/s11277-021-09038-2 doi: 10.1007/s11277-021-09038-2
    [28] A. K. Singh, S. V. N. Sreenivasu, U. S. B. K. Mahalaxmi, H. Sharma, D. D. Patil, E. Asenso, Hybrid feature-based disease detection in plant leaf using convolutional neural network, Bayesian optimized SVM and random forest classifier, J. Food Qual., 2022 (2022). https://doi.org/10.1155/2022/2845320 doi: 10.1155/2022/2845320
    [29] Makerere AI Lab, Bean disease dataset, 2020. Available from: https://github.com/AI-Lab-Makerere/ibean.
    [30] A. Mikołajczyk, M. Grochowski, Data augmentation for improving deep learning in image classification problem, in 2018 International interdisciplinary PhD workshop (IIPhDW), IEEE, (2018), 117–122. https://doi.org/10.1109/iiphdw.2018.8388338
    [31] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 1 (2022), 886–893. https://doi.org/10.1109/cvpr.2005.177
    [32] S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, et al., scikit-image: Image processing in Python, PeerJ, 2014. https://doi.org/10.7287/peerj.preprints.336v2 doi: 10.7287/peerj.preprints.336v2
    [33] W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, K. R. Müller, Explaining deep neural networks and beyond: A review of methods and applications, Proc. IEEE, 109 (2021), 247–278. https://doi.org/10.1109/jproc.2021.3060483 doi: 10.1109/jproc.2021.3060483
    [34] Tensorflow Keras: Layers, Retrieved October 6, 2022. Available from: https://www.tensorflow.org/api_docs/python/tf/keras/layers.
    [35] D. P. Kingma, J. A. Ba, J. Adam, A method for stochastic optimization, preprint, arXiv: 1412.6980. https://doi.org/10.48550/arXiv.1412.6980
    [36] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. C. Chen, MobileNetV2: Inverted residuals and linear bottlenecks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 4510–4520. https://doi.org/10.1109/cvpr.2018.00474
    [37] M. T. Riberio, S. Singh, C. Guestrin, "Why sould i trust you?" Explaining the predictions of any classifier, in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, (2016), 1135–1144. https://doi.org/10.1145/2939672.2939778
    [38] P. Bedi, P. Gole, PlantGhostNet: An efficient novel convolutional neural network model to identify plant diseases automatically, in 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), IEEE, (2021), 1–6. https://doi.org/10.1109/ICRITO51393.2021.9596543
    [39] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, Z. Xiaohua, T. Unterthiner, et al., An image is wort 16x16 words: Transformers for image recognition at scale, preprint, arXiv: 2010.11929. https://doi.org/10.48550/arXiv.2010.11929
    [40] Y. Borhani, J. Khoramdel, E. Najafi, A deep learning based approach for automated plant disease classification using vision transformer, Sci. Rep., 12 (2022), 1–10. https://doi.org/10.1038/s41598-022-15163-0 doi: 10.1038/s41598-022-15163-0
    [41] Y. Lu, S. Young, A survey of public datasets for computer vision tasks in precision agriculture, Comput. Electron. Agric., 178, (2020), 105760. https://doi.org/10.1016/j.compag.2020.105760 doi: 10.1016/j.compag.2020.105760
    [42] X. Zhai, A. Kolesnikov, N. Houlsby, L. Beyer, Scaling vision transformers, in Proceedings of the IEE/CVF Conference on Computer Vision and Pattern Recognition, (2022), 12104–12113.
    [43] J. M. P. Czarnecki, S. Samiappan, M. Zhou, C. D. McCraine, L. L. Wasson, Real-time automated classification of sky conditions using deep learning and edge computing, Remote Sens., 13 (2021), 3859. https://doi.org/10.3390/rs13193859 doi: 10.3390/rs13193859
    [44] S. Yu, L. Xie, Q. Huang, Inception convolutional vision transformers for plant disease identification, Internet Things, 21 (2023), 100650. https://doi.org/10.1016/j.iot.2022.100650 doi: 10.1016/j.iot.2022.100650
    [45] H. Xu, X. Su, D. Wang, CNN-based local vision transformer for covid-19 diagnosis, preprint, arXiv: 2207.02027. https://doi.org/10.48550/arXiv.2207.02027
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