Predicting the probability of wheat aphid occurrence using satellite remote sensing and meteorological data

Luo, Jiancheng; Huang, Wenjiang; Zhao, Jintong; Zhang, Jingcheng; Ma, Ronghua; Muyi, Huang

doi:10.1016/j.ijleo.2014.06.010

Cited by 11 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Uttar Pradesh State covers the major portion of agricultural land and contributes to the development of India. It is one of the leading states for wheat production in India [49,50]. Therefore, the continuous monitoring of agricultural land is required to protect the environment [51,52].…”

Section: Materials Study Areamentioning

confidence: 99%

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

et al. 2023

View full text Add to dashboard Cite

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring.

show abstract

Section: Materials Study Areamentioning

confidence: 99%

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

et al. 2023

View full text Add to dashboard Cite

show abstract

“…[ 8 , 16 , 17 , 18 , 19 ]. Some crop diseases and pests were predicted more successfully by integrating satellite imagery into the meteorological data based prediction models [ 3 , 20 , 21 ]. These results have encouraged us to use satellite imagery for monitoring the occurrence of powdery mildew in winter wheat.…”

Section: Introductionmentioning

confidence: 99%

Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Jing

Huang

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

show abstract

Modelling and monitoring house fly M. domestica using remote sensing data and geographic information system

Abutaleb

Yones

El-Shirbeny

et al. 2020

The Egyptian Journal of Remote Sensing and Space Science

View full text Add to dashboard Cite

Predicting the probability of wheat aphid occurrence using satellite remote sensing and meteorological data

Cited by 11 publications

References 21 publications

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Modelling and monitoring house fly M. domestica using remote sensing data and geographic information system

Contact Info

Product

Resources

About