A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network

Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation.This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.