Estimating the severity of apple mosaic disease with hyperspectral images

Ban, Sang-Woo; Tian, Minglu; Chang, Qingrui

doi:10.25165/j.ijabe.20191204.4524

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The above results demonstrate that the recognition rate of CACPNET can satisfy the requirement of real-time disease detection. Some advances in leaf disease identification have been made utilizing hyperspectral imaging (Ban et al, 2019;Nagasubramanian et al, 2019). However, the high weather or light requirements, professional operation, and extra-expensive equipment limit its overall development.…”

Section: Discussionmentioning

confidence: 99%

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Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Chen

Huang

et al. 2022

Front. Plant Sci.

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Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features’ extracting ability of plant leaf diseases. Based on the model’s optimum feature extraction condition, unimportant channels are removed to reduce the model’s parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.

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Section: Discussionmentioning

confidence: 99%

“…Some advances in leaf disease identification have been made utilizing hyperspectral imaging ( Ban et al., 2019 ; Nagasubramanian et al., 2019 ). However, the high weather or light requirements, professional operation, and extra-expensive equipment limit its overall development.…”

Section: Discussionmentioning

confidence: 99%

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Chen

Huang

et al. 2022

Front. Plant Sci.

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“…Normalization is required to easily define the membership functions for the fuzzy system input and output sets in values between 0 and 1, instead of 0 and 255, making the process more efficient [21,22]. The Mamdani type fuzzy inference system problem to identify a sick leave is modeled after the RGB value indicators, each one having 3 input sets (R,G,B color components) and the pixel status output set [23]: the first model has nine rules set representing the equivalence of a sane or ill pixel correlating it to the system's input and output, there are three triangular membership functions in the input sets established to depict low, medium, and high intensities for each RGB component as shown in Figure 3(a), the second system is defined by 17 rules and six membership functions in the input sets corresponding to the value intensity in the RGB scale (low, low-mid, mid-mid.…”

Section: Models' Design and Implementationmentioning

confidence: 99%

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

Velandia

Calderón

Lara

2021

IJECE

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This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess bean plants’ leaves for Xanthomonas campestris disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops.

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“…Partial least square regression (PLSR) and MLR can effectively restrain the problems of multi-collinearity and overfitting (Cho et al, 2007). Ban et al (2019) estimated the apple scab disease severity and correlated it with the SPSD chlorophyll reading with the help of multivariate modeling, PLSR. Ahmed et al (2019) have also studied different machine learning algorithms including that of KNN (K-Nearest Neighbour), J48 (decision tree), naive bayes, and logistic regression, decision tree algorithm for crop disease.…”

Section: Introductionmentioning

confidence: 99%

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

et al. 2023

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Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort. In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI). Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires–Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires–Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers’ fields for developing better disease management options.

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Estimating the severity of apple mosaic disease with hyperspectral images

Cited by 8 publications

References 29 publications

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

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