BLITE-SVR: New forecasting model for late blight on potato using support-vector regression

Gu, Yeong Hyeon; Yoo, Seong Joon; Park, C.J.; Kim, Y.H.; Park, S.K.; Kim, J.S.; Lim, Jin Hee

doi:10.1016/j.compag.2016.10.005

Cited by 45 publications

(27 citation statements)

References 18 publications

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“…Only a restricted number of these works gathered data with their own on-field IoT network [43][44][45]. The disease occurrence/severity under study was collected by performing field surveys or was provided by regional/local agricultural institutions [3,14,20,[46][47][48]. The data analyzed differed in volume, and were represented in different types and formats.…”

Section: Crop and Plant Disease Prediction 231 Data Sourcesmentioning

confidence: 99%

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Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Fenu

Malloci

2021

BDCC

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Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

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Section: Crop and Plant Disease Prediction 231 Data Sourcesmentioning

confidence: 99%

“…Similarly, continuous numerical values were used for the last purpose mentioned above [41,43]. Finally, only one paper predicted the first date of disease occurrence [47].…”

Section: Predicted Outputsmentioning

confidence: 99%

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Fenu

Malloci

2021

BDCC

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“…The reported results show that calable Color Descriptors outperforms all other reported descriptors. In [22] author proposed a potato late blight prediction model using Support Vector Regression (SVR) named as BLITE-SVR. The study used 13 kinds of abiotic factors mainly including temperature, humidity and evaporation which showed very high correlation to the first date of occurrence of late blight on potato leaves.…”

Section: Related Workmentioning

confidence: 99%

Plants Disease Phenotyping using Quinary Patterns as Texture Descriptor

Ahmad¹,

Shah²,

Irtaza³

2020

KSII TIIS

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Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0 o , 45 o , 90 o , and 135 o directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

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“…Potato (Solanum tuberosum) is one of major foods in the world [1] . It is the fourth major staple crop in China and thus important to resolve food-storage issues.…”

Section: Introductionmentioning

confidence: 99%

“…However, few studies have focused on detecting POD changes in leaves based on hyperspectral data. The objectives of this study were: (1) to optimize the hyperspectroscopy model of POD activity of potato leaves infected by late blight to predict POD activity changes during infection in an artificial climate chest; (2) to build a kinetic model of POD activity according to the hyperspectroscopy data and infection time under an artificial climate chest temperature; and (3) to predict late blight disease severity according to the POD activity.…”

Section: Introductionmentioning

confidence: 99%

Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data

Li¹,

Hu²

2019

International Journal of Agricultural and Biological Engineering

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Potato late blight, which is caused by Phytophthorainfestans (Mont.) de Bary, is a worldwide devastating disease for potato. It decreased yields of potato and caused unpredictable losses all over the world. Various simple statistical methods and forecasting models have been developed to predict and manage potato late blight. Meanwhile, there is a rising need to develop prediction models reflecting peroxidase (POD) activity, which is an important health index that varies with infection and correlated with stress resistance in plants. Thus, the aim of this research was to develop kinetic models to predict POD activity. Infection-induced changes in potato leaves stored in an artificial climate chest at 25°C were analyzed using hyperspectroscopy. Four prediction models were developed by using linear partial least squares (PLS) and nonlinear support vector machine (SVM) methods based on the full spectrum and effective wavelengths. The effective wavelengths were selected by the successive projection algorithm (SPA). In this study, the prediction model developed by means of SPA-SVM method obtained the best performance, with a R p (correlation coefficient of prediction) value of 0.923 and a RMSEp (root mean square error of prediction) value of 24.326. Five-order kinetics models according to the prediction model were developed, and late blight disease can be predicted using this model. This study provided a theoretical basis for the prediction of latencies of late blight.

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BLITE-SVR: New forecasting model for late blight on potato using support-vector regression

Cited by 45 publications

References 18 publications

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Plants Disease Phenotyping using Quinary Patterns as Texture Descriptor

Kinetic models of peroxidase activity in potato leaves infected with late blight based on hyperspectral data

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