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.

Comprehensive Prevention and Control Technology of Vegetable Diseases and Pests in Shandong Province

An in-depth research and practice has been conducted on vegetable diseases and pests in Shandong Province,and the principles of comprehensive and ecological control of diseases and pests are put forward,including agricultural control measures such as crop rotation,field cleaning,fertilizer and water management,physical control measures such as catching and killing,trapping,blocking,photoelectric energy treatment,biological control measures such as the use of natural enemies,pathogenic microorganisms,other beneficial organisms and metabolites,and scientific and rational chemical control measures.Comprehensive prevention and control not only controls vegetable diseases and pests effectively,but also protects the ecological environment.

Effects of Diseases and Pests on Quality, Industry and International Trade of Tea

Starting from the effects of diseases and pests on tea quality,tea industry and export trade,the status quo and control of tea diseases and pests,assurance and traceability of tea quality safety,tea industry and export trade development are analyzed in detail,and green prevention and control measures against tea diseases and pests are put forward combined with the status quo of tea in China.

Investigation on Diseases and Pests of Grape Under Rain Shelter Cultivation in Shandong Province

In order to make clear the effects of rain shelter cultivation on the incidence of diseases and pests of grape in Shandong Province,with Summer Black as the material,the diseases and pests of grape under rain shelter cultivation and open field cultivation in Tai'an and Jining were investigated.The results showed that under the rain shelter cultivation condition,the phenological period of grape delayed 5-8 d.The types of grape diseases and pests under the rain shelter cultivation were the same as those in the open filed cultivation.The most serious pests were Apolygus lucorum,followed by Lycorma delicatula,Erythreura apicalis and Thrips tabaci.The most serious leaf disease was downy mildew,and the most serious disease of grape berries was grape sour rot.Compared with the open field cultivation,the rain shelter cultivation could effectively reduce the incidence of pests and diseases in grape leaves and fruit.At the end of May 2018 and 2019,the occurrence rate of grape pests under the rain shelter cultivation in Tai'an reduced by 57.69%and 60.47%,respectively,and those in Jining reduced by 72.22%and 62.50%,respectively.In the middle of July of 2018 and 2019,the incidence of grape fruit diseases under the rain shelter cultivation in Tai'an reduced by 62.49%and 50.07%,respectively,and that in Jining reduced by 49.96%and 100%,respectively.

Occurrence and Damage Characteristics of Sugarcane Catastrophic Diseases and Pests in Menglian Sugarcane Area and Their Prevention and Control Strategies

In recent years,the conditions of continuous warm winter,more rain and high humidity are very beneficial to the breeding and survival of sugarcane diseases and pests.Coupled with the degradation of single varieties,more attention on new plants and less on ratoon cane and the unscientific and unreasonable use of chemical pesticides,the types of sugarcane diseases and pests in the Menglian sugarcane area are increasing day by day,and the damage is increasing year by year,resulting in a substantial reduction in production and sugar,and a serious disaster threat to sugarcane production.Based on the field survey and the current sugarcane production practice in Menglian,this paper systematically reviewed the occurrence and damage dynamics and outbreak reasons of catastrophic diseases and pests that seriously influence sugarcane production,and proposed corresponding prevention and control strategies and integrated prevention and control technology according to the occurrence and damage characteristics of catastrophic diseases and pests.

Integrated Control of Diseases and Pests of Horticultural Crops

In view of the occurrence and aggravation of diseases and pests of horticultural crops,the management process should follow the basic policy of"prevention first,comprehensive control".Starting from the ecological system,agricultural,biological,physical and chemical means should be comprehensively applied to control diseases and pests below the economic injury level.

Main Diseases and Pests of Jujube and Control Strategies in Shanxi

The main diseases and pests in the major growing area of jujube in Shanxi Province in recent years are investigated and studied,and several main diseases and pests are described.Based on the green prevention and control concept of crop diseases and pests proposed by the Ministry of Agriculture of China,the prevention and control of jujube diseases and pests are expounded from the perspectives of strengthening forecast,agricultural management,biological control and chemical control,in order to provide scientific basis for green development of jujube industry.

Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Tea plant stresses threaten the quality of tea seriously.The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation.In recent years,hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases,pests and some other stresses at the leaf level.However,the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale.In this study,based on the canopy-level hyperspectral imaging data,the methods for identifying and differentiating the three commonly occurred tea stresses(i.e.,the tea leafhopper,anthrax and sun burn)were studied.To account for the complexity of the canopy scenario,a stepwise detecting strategy was proposed that includes the process of background removal,identification of damaged areas and discrimination of stresses.Firstly,combining the successive projection algorithm(SPA)spectral analysis and K-means cluster analysis,the background and overexposed non-plant regions were removed from the image.Then,a rigorous sensitivity analysis and optimization were performed on various forms of spectral features,which yielded optimal features for detecting damaged areas(i.e.,YSV,Area,GI,CARI and NBNDVI)and optimal features for stresses discrimination(i.e.,MCARI,CI,LCI,RARS,TCI and VOG).Based on this information,the models for identifying damaged areas and those models for discriminating different stresses were established using K-nearest neighbor(KNN),Random Forest(RF)and Fisher discriminant analysis.The identification model achieved an accuracy over 95%,and the discrimination model achieved an accuracy over 93%for all stresses.The results suggested the feasibility of stress detection and differentiation using canopy-level hyperspectral imaging techniques,and indicated the potential for its extension over large areas.

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi- Spectral Image

Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.

Study on the Substrate Culture Techniques of Self-root Watermelon

In order to further improve the yield and quality of self-root watermelon and realize the continuous planting of self-root watermelon in the same field,this paper described the cultivation of self-root watermelon using a compound substrate as the cultivation medium.

Few-shot learning for biotic stress classification of coffee leaves

In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.