Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

中国农业航空植保产业技术创新发展战略

农业航空是现代农业的重要组成部分和反映农业现代化水平的重要标志之一。该文在分析中国农业现代化建设中对航空植保技术的需求及国内外航空植保发展现状的基础上,对中国航空植保产业体系进行了深入剖析。指出了制约中国农业航空植保产业发展的主要问题,包括现有农业航空政策法规体系不完善、配套核心科学技术研究不足、专业队伍人才匮乏、社会化服务体系不健全、与农业航空相适应的农田作业环境基础建设被忽略、制度上缺少支持农业航空发展的公益性安排等。并从提高航空植保作业适应性的多机型多作业方式、加大资金投入增强配套核心科学技术的攻关、以及出台有针对性的政策加强管理和规范等方面提出了大力推进中国农业航空植保产业快速健康发展的战略及对策建议。最后对未来3个五年计划内中国对航空植保技术的需求情况进行了预测。分析预测表明,中国农业航空产业是一个尚未真正启动的大产业,未来中国农业航空市场的需求将会有爆发性增长,拉动新增机型投入将达到465亿元以上。随着相关制度及配套核心技术的不断完善,中国农业航空产业必将得到健康、有序和高速发展,有利于实现农业病虫害统防统治,实现精准作业,极大地提速中国现代农业的进程。

中国农业航空植保产业技术创新发展战略

保证粮食安全是中国的基本国策。然而，在当前中国粮食作物生产过程中，植保仍以手工、半机械化操作为主。据统计，中国目前使用的植保机械以手动和小型机（电）动喷雾机为主，其中手动施药药械、背负式机动药械分别占国内植保机械保有量的93.07%和5.53%，拖拉机悬挂式植保机械约占0.57%，植保作业投入的劳力多、劳动强度大，施药人员中毒事件时有发生。据报道，广东省部分地区每天200元已请不到人工施药。目前国内农药用量越来越大，作业成本高，且浪费严重，资源有效利用率低下，作物产量和质量难以得到保障，同时带来严重的水土资源污染、生态系统失衡、农产品品质下降等问题，无法适应现代农业发展的要求。

Toxicity and feeding response of adult corn earworm (Lepidoptera: Noctuidae) to an organic spinosad formulation in sucrose solution

Adult corn earworm, Helicoverpa zea (Boddie), feeds on plant exudates soon after emergence from pupa in their natural habitat, and thereafter disperses to suitable host plants for reproduction. The intent of this study was to determine if EntrustTM, an organic formulation of spinosad, could be used in a behavioral-based pest management strategy to control H. zea in organic farming systems. In the laboratory, we evaluated the response of the corn earworm to Entrust mixed with sugar solution relative to ingestion, toxicity and proboscis extension. The sucrose solution served as a feeding stimulant and simulated the plant exudate. Lethal concentration of Entrust (LC50 with 95% CLs) for male corn earworm captured in pheromone-baited traps was 0.48 (0.43 - 0.53) mgL-1 for 24 h response. Mean lethal time was 2.56 ± 0.13 h with ingestion of Entrust at 50 mg·L-1. A lethal dose of Entrust at 1000 mg·L-1 inhibited neither ingestion nor proboscis extension response of the insect. A detailed study of the adult corn earworm in the laboratory relative to toxicity after ingestion of Entrust indicates that the pesticide has potential to control the insect when used in an insecticidal bait formulation as part of an attract-and- kill system. Field studies are needed to support the conclusion.

Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle(UAV)

With changing climate and farmland ecological conditions,pest outbreaks in agricultural landscapes are becoming more frequent,increasing the need for improved crop production tools and methods.UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products.This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection,and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals.Additionally,the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed,and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing,variable-rate technologies,and spray drift models are considered.Based on the reviewed literature,future developments and the impacts of these UAV technologies are projected.This review may inspire the innovation of the combined use of big data analytics and UAV technology,precision agricultural spraying technology,drift reduction technology,swarm UAV cooperative technology,and other supporting technologies for UAV-based aerial spraying for scientific research in the world.

Evaluating effective swath width and droplet distribution of aerial spraying systems on M-18B and Thrush 510G airplanes

Aerial spraying plays an important role in promoting agricultural production and protecting the biological environment due to its flexibility,high effectiveness,and large operational area per unit of time.In order to evaluate the performance parameters of the spraying systems on two fixed wing airplanes M-18B and Thrush 510G,the effective swath width and uniformity of droplet deposition under headwind flight were tested while the planes operated at the altitudes of 5 m and 4 m.The results showed that although wind velocities varied from 0.9 m/s to 4.6 m/s,and the directions of the atomizer switched upward and downward in eight flights,the effective swath widths were kept approximately at 27 m and 15 m for the M-18B and Thrush 510G,respectively,and the latter was more stable.In addition,through analyzing the coefficients of variation(CVs)of droplet distribution,it was found that the CVs of the M-18B were 39.57%,33.54%,47.95%,and 59.04% at wind velocities of 0.9,1.1,1.4 and 4.6 m/s,respectively,gradually enhancing with the increasing of wind speed;the CVs of Thrush 510G were 79.12%,46.19%,14.90%,and 48.69% at wind velocities of 1.3,2.3,3.0 and 3.4 m/s,respectively,which displayed the irregularity maybe due to change of instantaneous wind direction.Moreover,in terms of the CVs and features of droplet distribution uniformity for both airplanes in the spray swath,choosing smaller CV(20%-45%)as the standard of estimation,it was found that the Thrush 510G had a better uniform droplet distribution than the M-18B.The results provide a research foundation for promoting the development of aerial spraying in China.

Machine learning-based crop recognition from aerial remote sensing imagery

Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management decisions.This study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping area.Comparative analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features(layer spectral,geometry,and texture).Static recognition using layer spectral features had the highest accuracy of 75.4%in object-based classification,and dynamic recognition had the highest accuracy of 88.0%in object-based classification based on layer spectral and geometry features.Dynamic identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results,but also amplify the differences between different features.Object-based classification produced better results than pixel-based classification,and the three feature sets(layer spectral alone,layer spectral and geometry,and all three)resulted in only small differences in accuracy in object-based classification.Dynamic recognition combined with objectbased classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial imagery.The methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

The effect of terrain shadow, including the self and cast shadows, is one ofthe main obstacles for accurate retrieval of vegetation parameters byremote sensing in rugged terrains. A shadow- eliminated vegetation index(SEVI) was developed, which was computed from only red and nearinfrared top-of-atmosphere reflectance without other heterogeneous dataand topographic correction. After introduction of the conceptual modeland feature analysis of conventional wavebands, the SEVI was constructedby ratio vegetation index (RVI), shadow vegetation index (SVI) andadjustment factor (f (Δ)). Then three methods were used to validate theSEVI accuracy in elimination of terrain shadow effects, including relativeerror analysis, correlation analysis between the cosine of solar incidenceangle (cosi) and vegetation indices, and comparison analysis between SEVIand conventional vegetation indices with topographic correction. Thevalidation results based on 532 samples showed that the SEVI relativeerrors for self and cast shadows were 4.32% and 1.51% respectively. Thecoefficient of determination between cosi and SEVI was only 0.032 and thecoefficient of variation (std/mean) for SEVI was 12.59%. The results indicatethat the proposed SEVI effectively eliminated the effect of terrain shadowsand achieved similar or better results than conventional vegetation indiceswith topographic correction.

High resolution satellite imaging sensors for precision agriculture

The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.