Agricultural remote sensing big data:Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

Open-Source Hardware Is a Low-Cost Alternative for Scientific Instrumentation and Research

Scientific research requires the collection of data in order to study, monitor, analyze, describe, or understand a particular process or event. Data collection efforts are often a compromise: manual measurements can be time-consuming and labor-intensive, resulting in data being collected at a low frequency, while automating the data-collection process can reduce labor requirements and increase the frequency of measurements, but at the cost of added expense of electronic data-collecting instrumentation. Rapid advances in electronic technologies have resulted in a variety of new and inexpensive sensing, monitoring, and control capabilities which offer opportunities for implementation in agricultural and natural-resource research applications. An Open Source Hardware project called Arduino consists of a programmable microcontroller development platform, expansion capability through add-on boards, and a programming development environment for creating custom microcontroller software. All circuit-board and electronic component specifications, as well as the programming software, are open-source and freely available for anyone to use or modify. Inexpensive sensors and the Arduino development platform were used to develop several inexpensive, automated sensing and datalogging systems for use in agricultural and natural-resources related research projects. Systems were developed and implemented to monitor soil-moisture status of field crops for irrigation scheduling and crop-water use studies, to measure daily evaporation-pan water levels for quantifying evaporative demand, and to monitor environmental parameters under forested conditions. These studies demonstrate the usefulness of automated measurements, and offer guidance for other researchers in developing inexpensive sensing and monitoring systems to further their research.

Agricultural Water Footprint of Southern Highbush Blueberry Produced Commercially with Drip Irrigation and Sprinkler Frost Protection

A study was conducted from 2010 to 2017 to determine the water footprint for producing blueberries in the Entre Ríos province of Argentina. Three cultivars of southern highbush blueberry (hybrid cross of Vaccinium sp.) were evaluated in the study, including “Star”, “Emerald”, and “Snowchaser”. In each case, the plants were irrigated by drip and protected from frost using overhead sprinklers. Water requirements for irrigation and frost protection varied among the cultivars due to differences in the timing of flowering and fruit development. The annual water footprint for fruit production in each cultivar is expressed in units of cubic meters of water used to produce one ton of fresh fruit and ranged from 212 - 578 m3∙t−1 for “Star”, 296 - 985 m3∙t−1 for “Emerald”, and 536 - 4066 m3∙t−1 for “Snowchaser”. “Snowchaser” flowered earlier than the other cultivars and, therefore, needed more water for frost protection. “Star”, on the other hand, ripened the latest among the cultivars and required little to no water for frost protection. Frost protection required a minimum of 30 m3∙h−1 of water per hectare and in addition to drip irrigation was a major component of the water footprint.

Python Software Integrates with Microcontrollers and Electronic Hardware to Ease Development for Open-Source Research and Scientific Applications

Many options exist for developing and implementing monitoring systems for research and scientific applications. Commercially, available systems and devices, however, are usually built using proprietary tools and programming instructions, and often offer limited flexibility for end users. The use of open-source hardware and software has been embraced by the research and scientific communities and can be used to target unique data and information requirements. Development based on the Arduino microcontroller project has resulted in many successful applications, and the Arduino hardware and software environment continues to expand and become more powerful but can be intimidating for users with limited electronics or programming experience. The open-source Python language has gained in popularity and is being taught in schools and universities as an introduction to computer programming and software development due to its simple structure, ease of use, and large standard library of functions. A project called CircuitPython was developed to extend the use of Python to programming hardware devices such as programmable microcontrollers and maintains much of the original Python lang<span>uage and features, with additional support for accessing and controlling microcontroller hardware. The objective of the work reported here is to discuss the CircuitPython programming language and demonstrate its use in the development of research and scientific applications. Several open-source sensing and monitoring systems developed using open-source hardware and the open-source CircuitPython programming language are presented and described.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Genetics of biochemical attributes regulating morpho-physiology of upland cotton under high temperature conditions

Background Cotton is a strategically important fibre crop for global textile industry.It profoundly impacts several countries’industrial and agricultural sectors.Sustainable cotton production is continuously threatened by the unpre-dictable changes in climate,specifically high temperatures.Breeding heat-tolerant,high-yielding cotton cultivars with wide adaptability to be grown in the regions with rising temperatures is one of the primary objectives of modern cotton breeding programmes.Therefore,the main objective of the current study is to figure out the effective breed-ing approach to imparting heat tolerance as well as the judicious utilization of commercially significant and stress-tolerant attributes in cotton breeding.Initially,the two most notable heat-susceptible(FH-115 and NIAB Kiran)and tolerant(IUB-13 and GH-Mubarak)cotton cultivars were spotted to develop filial and backcross populations to accom-plish the preceding study objectives.The heat tolerant cultivars were screened on the basis of various morphological(seed cotton yield per plant,ginning turnout percentage),physiological(pollen viability,cell membrane thermostabil-ity)and biochemical(peroxidase activity,proline content,hydrogen peroxide content)parameters.Results The results clearly exhibited that heat stress consequently had a detrimental impact on every studied plant trait,as revealed by the ability of crossing and their backcross populations to tolerate high temperatures.However,when considering overall yield,biochemical,and physiological traits,the IUB-13×FH-115 cross went over particularly well at both normal and high temperature conditions.Moreover,overall seed cotton yield per plant exhibited a posi-tive correlation with both pollen viability and antioxidant levels(POD activity and proline content).Conclusions Selection from segregation population and criteria involving pollen viability and antioxidant levels concluded to be an effective strategy for the screening of heat-tolerant cotton germplasms.Therefore,understanding acquired from this study can assist breeders identifying traits that should be prioritized in order to develop climate resilient cotton cultivars.

Is magnesium deficiency the major cause of needle chlorosis of Pinus taeda in Brazil?

Needle chlorosis(NC)in Pinus taeda L.systems in Brazil becomes more frequent after second and third harvest rotation cycles.In a study to identify factors contributing to yellowing needle chorosis(YNC),trees were grown in soils originating from contrasting parent materials,and soils and needles(whole,green and chlorotic portions)from 1-and 2-year-old branches and the first and second needle flush release at four sites with YNC on P.taeda were analyzed for various elements and properties.All soils had very low base levels(Ca^(2+),Mg^(2+)and K^(+))and P,suggesting a possible lack of multiple elements.YNC symptoms started at needle tips,then extended toward the needle base with time.First flush needles had longer portions with YNC than second flush needles did.Needles from the lower crown also had more symptoms along their length than those higher in the canopy.Symptoms were similar to those reported for Mg.In chlorotic portions,Mg and Ca concentrations were well below critical values;in particular,Mg levels were only one third of the critical value of 0.3 g kg^(-1).Collectively,results suggest that Mg deficiency is the primary reason for NC of P.taeda in various parent soils in Brazil.

A glycine-rich nuclear effector VdCE51 of Verticillium dahliae suppresses plant immune responses by inhibiting the accumulation of GhTRXH2

Verticillium dahliae is an important soil-borne fungal pathogen that causes great yield losses in many cash crops.Effectors of this fungus are known to regulate plant immunity but the mechanism much remains unclear.A glycine-rich nuclear effector,VdCE51,was able to suppress immune responses in tobacco against Botrytis cinerea and Sclerotinia sclerotiorum.This effector was a required factor for full virulence of V.dahliae,and its nuclear localization was a requisite for suppressing plant immunity.The thioredoxin GhTRXH2,identified as a positive regulator of plant immunity,was a host target of VdCE51.Our findings show a virulence regulating mechanism whereby the secreted nuclear effector VdCE51 interferes with the transcription of PR genes,and the SA signaling pathway by inhibiting the accumulation of GhTRXH2,thus suppressing plant immunity.

Evaluation of Pesticidal Activities of Lignans Isolated from Piper cubeba Fruits

The identification of natural, plant-derived compounds with pesticidal properties is crucial for developing environmentally sustainable alternatives to synthetic pesticides. In this study, four major lignans—dihydroclusin, cubebin, clusin, and yatein—were isolated from the crude extract of Piper cubeba fruit. Phytotoxicity assays revealed herbicidal activity against Agrostis stolonifera, with dihydroclusin and clusin exhibiting the highest efficacy, inhibiting seed germination by 50% and showing IC50 values of 2.9 µM and 45 µM, respectively, against Lemna paucicostata. Additionally, all compounds, except dihydroclusin, demonstrated fungicidal activity against the strawberry anthracnose pathogen Colletotrichum fragariae. Moreover, only dihydroclusin exhibited larvicidal activity against Aedes aegypti, causing 96% mortality of mosquito larvae at the 100-ppm concentration tested. These findings highlight the broad-spectrum bioactivity of Piper cubeba lignans, suggesting their potential as alternative agents of synthetic pesticides for managing agricultural pests.

面向精准农业的农田土壤成分实时测定研究进展

为了开发土壤成分田间实时测定仪,在分析土壤养分实时测定需求的基础上,归纳了土壤主要成分测试项目及常规化学分析法、基于光电分色和电化学传感器测定法、土壤电导率间接测定法,重点对近红外光谱分析法测定土壤成分的基本原理、实验室研究、仪器原型开发和田间试验结果等进行论述。所阐述的方法、仪器原型和提出尚需解决的几个科学问题,对研究、开发土壤成分实时测定仪具有一定的指导作用和参考价值。

哈密瓜细菌性果斑病菌快速检测方法的建立

【目的】哈密瓜细菌性果斑病在中国为新发生的病害,其病原菌为燕麦食酸菌西瓜亚种(Acidovorax avenae subsp.Citrulli),危害瓜果造成品质下降。该病为典型的种传细菌性病害,因此种子检疫成为防治此病害的重要手段。【方法】通过实验,将生物学、免疫学和分子生物学检测技术有机结合,建立了一套针对哈密瓜果斑病的种子带菌检测方法Bio-IMS-Real-timePCR。【结果】经过人工模拟种子带菌检测实验,结果表明该方法可成功地检测出1000粒种子中的1粒带菌种子(带菌量约为1.04×105CFU/种子,经计算,可以检测的最初浓度为2CFU·ml-1ASCM培养液),且信号较强。【结论】该检测方法的建立,大大提高了对哈密瓜细菌性果斑病菌检测的精度,也为生产实践中哈密瓜果斑病的防治提供了重要的技术支持。

猪胃黏蛋白偶联磁珠和聚乙二醇富集检测青葱和葡萄中诺如病毒的比较研究

目的:以青葱与葡萄为材料,建立以猪胃黏蛋白偶联磁珠(PGM-MB)和聚乙二醇8000(PEG8000)富集检测水果、蔬菜中诺如病毒的方法。方法:确定病毒原液中的相对病毒量,梯度稀释病毒原液并进行实时荧光-聚合酶链式反应检测,以每个反应管内的荧光信号达到设定的域值时所经历的循环数值与病毒量(实时荧光-聚合酶链式反应单位数)的常用对数值绘制标准曲线和线性方程;人工接种诺如病毒于青葱与葡萄表面,洗脱后,分别用PEG8000和PGM-MB富集诺如病毒,实时荧光-聚合酶链式反应扩增,用标准曲线对回收的病毒进行相对定量。结果:基质为青葱时,高接种量条件下,两种富集方法的病毒回收效果相当,低接种量下,PGM-MB法的富集回收率高于PEG8000法,且PGM-MB的检测下限更低;基质为葡萄时,PGM-MB法的富集回收率均高于PEG8000法,且检测下限更低。结论:PGM-MB富集效果良好,快速方便,适合应用于水果和蔬菜中的诺如病毒的富集检测。

光肩星天牛种群扩散规律的研究

为了探讨光肩星天牛成虫在自然界中种群扩散的模式和种群数量变动等问题,在甘肃省永靖县采用标记-回捕的方法进行了光肩星天牛的扩散规律研究。系统收集了光肩星天牛成虫种群在时间序列过程中的扩散资料,按照动态分析方法,对天牛成虫在杨树农田林网中的扩散规律进行了研究,并对影响天牛成虫扩散的因子进行了分析。结果表明:种群扩散有明显的方向性,由环境因素引起。对气象因素分析表明扩散格局主要受风向影响,各方向的回收数量都与其相同风向呈负相关,而与相反风向呈正相关,但与风速、温度和相对湿度关系不密切。成虫的扩散密度随距离和时间的增加而减少,其扩散距离与数量的关系可以用数学模型y=4 203.568 3x-1.117 56来表示。种群总体的平均扩散距离为423.7 m。雌虫和雄虫在扩散的方向和距离上无差异,但在回捕率上雄虫高于雌虫。光肩星天牛在农田防护林中可扩散至2 000 m以上,观测到的最远扩散距离为2 644 m。

基于空间可分辨光谱的番茄成熟度判别方法研究

对基于空间可分辨光谱的番茄成熟度分类判别方法进行了试验研究。首先根据番茄的内部颜色,将600个番茄分为6个不同成熟度(green,breaker,turning,pink,light red and red),然后用自行开发的多通道高光谱成像探头采集番茄的空间可分辨(SR)光谱,建立基于空间可分辨光谱的番茄成熟度偏最小二乘判别(PLSDA)模型和支持向量机判别(SVMDA)模型。结果显示,对于PLSDA模型,SR光谱15为最佳分类光谱,分类正确率达到81.3%;对于SVMDA模型,SR光谱10为最佳预测分类光谱,分类正确率为86.3%。对六个成熟度等级番茄的判别分类,SVMDA模型要明显优于PLSDA模型。此外,相对于较小的光源-检测器距离SR光谱,较大的光源-检测器距离SR光谱可以获得更好的判别效果,显示出空间可分辨光谱在果蔬品质检测方面的应用潜力。

蜜瓜和西瓜果汁的射频介电特性及其与糖度的关系

为了解果汁的介电特性与其主要内部品质——糖度之间的关系,该文利用末端开路的同轴探头和阻抗分析仪测量了10 MHz～1.8 GHz的频率范围内不同成熟度的3个品种密瓜汁和4个品种西瓜汁的介电特性,同时测量了反映果汁糖度的可溶性固形物含量和果肉的含水率,分析了介电特性和糖度间的关系。结果显示:密瓜和西瓜果汁的相对介电常数和介质损耗因数皆随频率的增大单调减小,品种间的差异对介电特性有影响;1.8 GHz下密瓜汁的损耗角正切与糖度有较好的线性关系,其决定系数为0.784,但西瓜汁的介电特性与糖度的相关性较差;密瓜和西瓜果汁的糖度与果肉的含水率有很好的线性相关性,其决定系数分别为0.973和0.906。该研究将为基于介电特性的果汁糖度检测仪的开发提供了研究基础。

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。

土壤表面微形貌加工技术及其在南方丘陵山区坡耕地的应用前景

坡耕地的水土流失和季节性干旱对地区生态环境和农业生产造成严重负面影响。近年来,一种通过改变田间微观地貌以实现集雨目的的新型整地方式,即土壤表面微形貌加工在国外得到快速发展,并成为解决这一问题的关键。本文针对土壤表面微形貌加工技术的作业原理、在国内外不同地区作业实现的作业效果、当前作业采用的主要机具进行了系统的描述和归纳,阐明该技术可有效控制坡耕地水土流失并提高水资源利用效率。结合南方丘陵山区坡耕地实际特点,对土壤表面微形貌加工技术的应用前景和潜在价值进行了探讨和展望,认为该技术可对云南省坡耕地降雨地表径流加以综合利用,以达到控制水土流失和缓解区域干旱缺水的双重目的。

福氏志贺菌mdoC基因对其在洗菜水中生长及生物膜形成的影响

【目的】研究福氏志贺菌mdoC基因对细菌在不同洗菜水中生长及生物膜形成的影响。【方法】以福氏志贺菌野生型和敲除mdoC基因的opgC突变体为出发菌株,采用生长曲线法和结晶紫染色法,在低渗透压及正常渗透压的菠菜、芹菜、生菜及白菜洗菜水中,研究福氏志贺菌野生型和opgC突变体生长及生物膜的产生能力。【结果】在不同洗菜水中,福氏志贺菌opgC突变体生长速率明显较野生型慢,加盐提高渗透压之后,野生型和opgC突变体稳定生长期均提前。野生型和opgC突变体的生物膜产量在菠菜水、生菜水和白菜水中有显著区别;提高渗透压之后,福氏志贺菌野生型在菠菜水中的生物膜产量显著提高,而在生菜水和白菜水中生物膜产量显著下降,opgC突变体在菠菜水、生菜水和白菜水中生物膜产量均显著提高。【结论】低渗条件下,福氏志贺菌mdoC基因的缺失显著地延缓了细菌生长。提高渗透压后,在菠菜、生菜和白菜洗菜水中,mdoC基因的缺失促进了生物膜的形成。

波长比和近红外光谱的番茄品质检测方法

番茄的可溶性固形物(SSC)、pH值和坚实度(Firmness)是决定番茄的口感及收获后品质的主要因素。提出一种基于波长比和近红外光谱的番茄SSC,pH和坚实度检测方法。首先用Vis/SWNIR便携式光谱仪(波长:400~1 100nm)和中波近红外便携式光谱仪(波长:900~1 683nm)在相互作用模式下,对6个不同成熟度的600个番茄样本进行光谱采集,对所采集的光谱用波长比法和波长比+自动缩放法进行预处理后,分别建立番茄SSC,pH和坚实度的预测模型,比较单一自动缩放、单一波长比、波长比+自动缩放及不做预处理四种方法的预测结果。结果显示,波长比法结合自动缩放预处理可有效提高可见/短波近红外光谱对SSC,pH和坚实度的预测精度(r_p=0.779,0.796和0.917);波长比法+自动缩放法可提高中波近红外光谱对SSC的预测效果(rp=0.818)。这说明波长比法在优化和处理番茄光谱信息方面具有一定的潜力。

Thrips: Pests of Concern to China and the United States

Thrips are among the most important agricultural pests globally because of the damage inflicted by their oviposition, feeding, and ability to transmit plant viruses. Because of their invasiveness, a number of pest species are common to both China and the United States and present significant challenges to growers of a wide range of crops in both countries. Among the pest thrips common to both countries are four of the major global thrips pests, Frankliniella occidentalis （Pergande）, Scirtothrips dorsalis Hood, Thrips palmi Karny, and Thrips tabaci Lindeman. This review addresses characteristics that enable thrips to be such damaging pests and how biological attributes of thrips create challenges for their management. Despite these challenges, a number of successful management tactics have been developed for various cropping systems. We discuss some of these tactics that have been developed, including the use of cultural controls, biological controls, and judicious use of insecticides that do not disrupt overall pest management programs. The exchange of this type of information will help to facilitate management of pest thrips, especially in regions where species have recently invaded. A prime example is F. occidentalis, the western flower thrips, which is native to the United States, but has recently invaded China. Therefore, management tactics developed in the United States can be adapted to China. Because further success in management of thrips requires a thorough understanding of thrips ecology, we discuss areas of future research and emphasize the importance of collaboration among different countries to enhance our overall understanding of the biology and ecology of thrips and to improve management programs for these widespread pests.