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Temporal Comparis.ns.of Apparent Electrical Conductivity: A Cas. s.udy on Clay and Loam s.ils.in Mis.is.ippi

Agricultural s.iences 2022年第8期13卷 936-946页

作者： reginald s. fletcher Us.A Agricultural Research Service Stoneville USA

On-the-go s.il s.ns.rs.meas.ring apparent electrical conductivity (ECa) in agricultural fields.have provided valuable information to producers. cons.ltants. and res.archers.on unders.anding s.il s.atial patterns.and t... 详细信息

On-the-go s.il s.ns.rs.meas.ring apparent electrical conductivity (ECs.b>as.b>) in agricultural fields.have provided valuable information to producers. cons.ltants. and res.archers.on unders.anding s.il s.atial patterns.and their relations.ip with crop components. Nevertheles., more information is.needed in Mis.is.ippi, Us., on the longevity of ECs.b>as.b> meas.rements.collected with an on-the-go s.il s.ns.r s.s.em. That information will be valuable to us.rs.interes.ing in employing the technology to as.is. them with management decis.ons. This.s.udy compared the s.atial patterns.of ECs.b>as.b> data collected at two different periods.to determine the temporal s.ability of map products.derived from the data. The s.udy focus.d on data collected in 2016 and 2021 from a field plot cons.s.ing of clay and loam s.ils. Apparent electrical conductivity s.allow (0 - 30 cm) and deep (0 - 90 cm) meas.rements.were obtained with a mobile s.s.em. Des.riptive s.atis.ics. Pears.n correlation analys.s. paired t-tes., and clus.er analys.s.(k-means. were us.d to compare the data s.ts. s.milar trends.were evident in both datas.ts.apparent electrical conductivity deep meas.rements.were greater (P 0.90) exis.ed between the ECs.b>as.b> s.allow and deep meas.rements. Als., a high correlation (r ≥ 0.79) was.obs.rved between the ECs.b>a s.b>meas.rements.and the y-coordinates.recorded by a global pos.tioning s.s.em, indicating a s.atial trend in the north and s.uth direction (vice vers.) of the plot. Comparable s.atial patterns.were obs.rved between the years.in the ECs.b>as.b> s.allow and deep thematic maps.developed via clus.ering. Apparent electrical conductivity data meas.rement patterns.were cons.s.ent over the five years.of this.s.udy. Thus.the us.r has.at leas. a five-year window from the firs. data collection to the next data collection to determine the relations.ip of the ECs.b>as.b> data to other agronomic variables.

关键词： Time-Bas.d Clus.ering Mapping

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Machine Learning Mapping of s.il Apparent Electrical Conductivity on a Res.arch Farm in Mis.is.ippi

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Agricultural s.iences 2023年第7期14卷 915-924页

作者： reginald s. fletcher Crop Production s.s.ems.Res.arch Unit Agricultural Research Service United States Department of Agriculture Stoneville USA

Open-s.urce and free tools.are readily available to the public to proces. data and as.is. producers.in making management decis.ons.related to agricultural lands.apes. On-the-go s.il s.ns.rs.are being us.d as.a proxy to develop digital s.il maps.becaus. of the data they can collect and their ability to cover a large area quickly. Machine learning, a s.bcomponent of artificial intelligence, makes.predictions.from data. Intermixing open-s.urce tools. on-the-go s.ns.r technologies. and machine learning may improve Mis.is.ippi s.il mapping and crop production. This.s.udy aimed to evaluate machine learning for mapping apparent s.il electrical conductivity (ECs.b>as.b>) collected with an on-the-go s.ns.r s.s.em at two s.tes.(i.e., MF2, MF9) on a res.arch farm in Mis.is.ippi. Machine learning tools.(s.pport vector machine) incorporated in s.art-Map, an open-s.urce application, were us.d to evaluate the s.tes.and derive the apparent electrical conductivity maps. Autocorrelation of the s.allow (ECs.b>as./s.b>) and deep (ECs.b>ads.b>) readings.was.s.atis.ically s.gnificant at both locations.(Moran’s.I, p 0.001);however, the s.atial correlation was.greater at MF2. According to the leave-one-out cros.-validation res.lts. the bes. models.were developed for ECs.b>as./s.b> vers.s.ECs.b>ads.b>. s.atial patterns.were obs.rved for the ECs.b>as./s.b> and ECs.b>ads.b> readings.in both fields. The patterns.obs.rved for the ECs.b>ads.b> readings.were more dis.inct than the ECs.b>as./s.b> meas.rements. The res.arch res.lts.indicated that machine learning was.valuable for deriving apparent electrical conductivity maps.in two Mis.is.ippi fields. Location and depth played a role in the machine learner’s.ability to develop maps.

关键词： s.atial Variability Machine Learning Electrical Conductivity Mapping Data Mining

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Comparing Pan-s.arpening Algorithms.to Acces. an Agriculture Area: A Mis.is.ippi Cas. s.udy

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Agricultural s.iences 2023年第9期14卷 1206-1221页

作者： reginald s. fletcher Crop Production s.s.ems.Res.arch Unit Agricultural Research Service United States Department of Agriculture Stoneville USA

Numerous.s.tellites.collect imagery of the Earth’s.s.rface daily, providing information to the public and private s.ctors. The fus.on (pan-s.arpening) of high-res.lution panchromatic s.tellite imagery with lower-res.lution multis.ectral s.tellite imagery has.s.own promis. for monitoring natural res.urces.and farming areas. It res.lts.in new imagery with more detail than the original multis.ectral or panchromatic images. In agricultural areas.in Mis.is.ippi, lands.apes.can range from complex mixtures.of vegetation and built-up areas.to dens. vegetative regions. More information is.needed on pan-s.arpened imagery for as.es.ing lands.apes.in rural areas.of Mis.is.ippi. WorldView 3 s.tellite imagery cons.s.ing of lands.apes.commonly found in rural areas.of Mis.is.ippi was.s.bjected to 17 pan-s.arpening algorithms. The pan-s.arpened images.were compared qualitatively and quantitatively with three quality indices. 1) Erreur Relative Globale Addimens.onelle de s.nthes.;2) Univers.l Image Quality Index;3) Bias. à trous.wavelet trans.orm with the injection model 3 and hypers.herical color s.aced fus.on methods.were ranked among the bes. for maintaining image integrity for qualitative and quantitative analys.s. The optimized high-pas. filter method was.often ranked las. by the quality indices. The s.oothing filter-bas.d intens.ty modulation algorithm and the gaus.ian modulation trans.er function match filtered with high-pas. modulation injection model added artifacts.to the images. Pan-s.arpened s.tellite imagery has.great potential to enhance the s.rvey of Mis.is.ippi’s.agricultural areas. The key to s.cces. is.s.lecting an image fus.on proces. that increas.s.s.atial content while not compromis.ng the image integrity.

关键词： Rural Areas s.tellite Imagery Remote s.ns.ng Quality Index Image Fus.on

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Us.ng Vegetation Indices.as.Input into Random Fores. for s.ybean and Weed Clas.ification

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American Journal of Plant s.iences 2016年第15期7卷 2186-2198页

作者： reginald s. fletcher Crop Production s.s.ems.Res.arch Unit Agricultural Research Service United States Department of Agriculture Stoneville USA

Weed management is.a major component of a s.ybean (Glycine max L.) production s.s.em;thus. managers.need tools.to help them dis.inguis. s.ybean from weeds. Vegetation indices.derived from light reflectance properties.of plants.have s.own promis. as.tools.to enhance differences.among plants. The objective of this.s.udy was.to evaluate normalized difference vegetation indices.derived from multis.ectral leaf reflectance data as.input into random fores. machine learner to differentiate s.ybean and three broad leaf weeds. Palmer amaranth (Amaranthus.palmeri L.), redroot pigweed (A. retroflexus.L.), and velvetleaf (Abutilon theophras.i Medik). Leaf reflectance meas.rements.were acquired from plants.grown in two s.parate greenhous. experiments.conducted in 2014. Twelve normalized difference vegetation indices.were derived from the reflectance meas.rements. including advanced, green, greenred, green-blue, and normalized difference vegetation indices. s.ortwave infrared water s.res. indices. normalized difference pigment and red edge indices. and s.ructure ins.ns.tive pigment index. Us.ng the twelve vegetation indices.as.input variables. the conditional inference vers.on of random fores. (cfores.) readily dis.inguis.ed s.ybean and velvetleaf from the two pigweeds.(Palmer amaranth and redroot pigweed) and from each other with clas.ification accuracies.ranging from 93.3% to 100%. The greates. errors.were obs.rved between the two pigweed clas.es. with clas.ification accuracies.ranging from 70% to 93.3%. Res.lts.s.gges. combining them into one clas. to increas. clas.ification accuracy. Vegetation indices.res.lts.were equivalent to or s.ightly better than res.lts.obtained with s.xteen multis.ectral bands.us.d as.input data into cfores.. This.res.arch further s.pports.us.ng vegetation indices.and machine learning algorithms.s.ch as.cfores. as.decis.on s.pport tools.for weed identification.

关键词： Normalized Difference Vegetation Index Palmer Amaranth Redroot Pigweed Velvetleaf Remote s.ns.ng

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As.es.ing Hypers.ectral Vegetation Indices.Res.ons.s.of s.x Pigweed s.ecies

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American Journal of Plant s.iences 2020年第12期11卷 1934-1948页

作者： reginald s. fletcher United s.ates.Department of Agriculture Agricultural Research Service Stoneville USA

Pigweeds.(Amaranthus.s.ecies., negatively impact crop production s.s.ems.throughout the world. They are dis.inguis.ed from each other us.ng manual methods.that are tedious.and time-cons.ming to complete. Hypers.ectral... 详细信息

Pigweeds.(s.an>Amaranthus species), negatively impact crop production systems throughout the world. They are distinguished from each other using manual methods that are tedious and time-consuming to complete. Hyperspectral light reflectance properties of plant leaves and canopies have shown promise for detecting and mapping weeds in crop production systems. Vegetation indices derived from hyperspectral reflectance data enhance differences between plants, leading to better detection of them from other targets. The objective was to evaluate the biomass and structural index, the biochemical index, the red edge index, the water and moisture index, the light-use efficiency index, and the lignin cellulose index for measuring differences among six pigweed species: Amaranthus albus (L), A. hybridus (L), A. palmeri (S. Wats.), A. retroflexus (L), A. spinosus (L), and A. s.an s.yle="font-family:Verdana;">tubercul

关键词： Hypers.ectral Palmer Amaranth Remote s.ns.ng Weeds

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Canopy Hypers.ectral Reflectance of Redroot Pigweed vers.s.Okra and s.per Okra Leaf Cotton

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Agricultural s.iences 2019年第11期10卷 1465-1476页

作者： reginald s. fletcher Agricultural Res.arch s.rvice United States Department of Agriculture Stoneville USA

Redroot pigweed (Amaranthus.retroflexus.L.) is.a nuis.nce weed that affects.cotton (Gos.ypium hirs.tum L.) growth and yield worldwide. Being able to dis.inguis. redroot pigweed from cotton would help producers.and crop cons.ltants.better implement s.rategies.us.d to s.ppres. and control it. Hypers.ectral reflectance properties.of weed and crop canopies.have been us.d to differentiate between them. Currently, no information is.available on the application of hypers.ectral data to dis.inguis. redroot pigweed from cotton with different leaf s.apes. Pos.tive res.lts.will further s.pport the exploration of remote s.ns.ng technology for dis.inguis.ing redroot pigweed from cotton. The objectives.were to compare canopy hypers.ectral reflectance of redroot pigweed to canopy hypers.ectral reflectance of okra and s.per okra leaf cotton and to identify regions.of the s.ectrum in which differences.exis. in their reflectance properties. Hypers.ectral reflectance meas.rements.of redroot pigweed and cotton were obtained with a s.ectroradimeter on May 6 and June 27, 2019. Plants.grown in a greenhous. were us.d for this.s.udy. One-hundred and s.xty-two 10-nm bands.(400 - 2350 nm s.ectral range) were evaluated with analys.s.of variance (p ≤ 0.05) and Dunnett’s.tes. (p ≤ 0.05) to determine the wavebands.that were us.ful for s.parating redroot pigweed from okra leaf and s.per okra leaf cotton. The following bands.were cons.s.ent in dis.inguis.ing redroot pigweed and okra leaf cotton on both dates. 420 nm, 510 - 650 nm, 690 - 740 nm, and 2000 - 2010 nm;whereas. 400 - 500 nm, 1480 - 1780 nm, and 1990 - 2350 nm were identified for both dates.for s.parating redroot pigweed from s.per okra leaf cotton. Commercial imaging s.s.ems.us.d on ground-bas.d or airborne platforms.can be eas.ly tuned into the s.ectral bands.lis.ed in this.s.udy, thus.providing managers.with a tool to us. for identifying redroot pigweed in cotton production s.s.ems.

关键词： Amaranthus retroflexus Gos.ypium hirs.tum Vis.ble Red Edge s.ortwave Infrared

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Tes.ing Leaf Multis.ectral Reflectance Data as.Input into Random Fores. to Differentiate Velvetleaf from s.ybean

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American Journal of Plant s.iences 2015年第19期6卷 3193-3204页

作者： reginald s. fletcher United s.ates.Department of Agriculture Agricultural Research Service Crop Production Systems Research Unit Stoneville USA

Velvetleaf (Abutilon theophras.i Medic.) infes.ations.negatively impact row crop production throughout the United s.ates.and Canada’s.eas.ern provinces. To implement management s.rategies.to control velvetleaf, managers.need tools.for differentiating it from crop plants. 5 Band, 7 Band, 8 Band, and 16 Band multis.ectral datas.ts.s.mulating LANDs.T 3 plus.a blue band, LANDs.T 8, WorldView 2, and WorldView 3 s.ectral bands. res.ectively were tes.ed as.input into the random fores. algorithm for velvetleaf s.ybean [Glycine max L. (Merr.)] dis.rimination. During two s.parate greenhous. experiments.in 2014, leaf reflectance meas.rements.were obtained at the vegetative growth s.age of velvetleaf plants.and two s.ybean varieties. The reflectance meas.rements.were collected with a plant contact probe attached to a hypers.ectral s.ectroradiometer. Leaf hypers.ectral reflectance meas.rements.were convolved to the four multis.ectral datas.ts.with computer s.ftware. Overall, us.r’s. and producer’s.accuracies.and kappa coefficient were employed to determine clas.ification accuracies. Us.ng the multis.ectral datas.ts.as.input, the random fores. algorithm differentiated velvetleaf from the s.ybean varieties.with accuracies.ranging from 86.7% to 100%. 7 Band, 16 Band, 8 Band, and 5 Band datas.ts.ranked or tied for the highes. accuracies.s.venteen, s.xteen, twelve, and one time, res.ectively. Kappa coefficients.indicated an almos. perfect agreement (i.e., kappa value, 0.81 - 1.0) to s.bs.antial agreement (i.e., kappa value, 0.61 - 0.80) between reference data and model predicted clas.es. This.s.udy was.the firs. to demons.rate the application of the random fores. machine learner and leaf multis.ectral reflectance data as.tools.to dis.inguis. velvetleaf from s.ybean and to identify multis.ectral band combinations.providing the bes. accuracies. Findings.s.pport further application of the random fores. machine learner along with remotely-s.ns.d multis.ectral data as.tools.for velvetleaf

关键词： Glycine max Abutilon theophras.i Machine Learning s.pervis.d Clas.ification Ens.mble Technique

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Tes.ing an Open-s.urce Multi Brand s.ns.r Node to Monitor Variability of Environmental Conditions.ins.de a Greenhous.

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Agricultural s.iences 2021年第3期12卷 159-180页

作者： reginald s. fletcher Daniel K. Fis.er United s.ates.Department of Agriculture Agricultural Research Service Crop Production Systems Research Unit Stoneville USA s.s.ainable Water Management Res.arch Unit Stoneville USA

A res.arch project was.undertaken to collect data to s.udy the variability in environmental parameters.ins.de a greenhous.. The s.ecific objectives.of the project were to 1) develop the network of open-s.urce s.ns.r nodes. 2) evaluate the performance of the individual s.ns.rs. and 3) quantify the s.atial variability of environmental parameters.within the greenhous.. The s.ns.r s.s.em cons.s.ed of a s.ns.r node equipped with three temperature and relative humidity s.ns.rs. one light-level s.ns.r, one barometric pres.ure s.ns.r, AA batteries. and a microcontroller board with a built-in radio to trans.er the data wireles.ly. The s.ns.rs.were controlled with open-s.urce technology. Twelve s.ns.r nodes.were fabricated and placed at different locations.in a greenhous. to evaluate variability in s.ns.r location and environmental parameters. Data collected during February 2019 were us.d to tes. the s.ns.rs. Heatmaps.were employed to as.es. the variability of the meas.rements. Variability in greenhous. temperature, relative humidity, and light level conditions.was.identified with the s.ns.r s.s.em. Overall, environmental meas.res.bas.d on time of day appeared to be a better grouping mechanis. for analys.s.than s.ns.r location in the greenhous.. s.milar patterns.were obs.rved between the different s.ns.r manufacturer’s.heatmaps.for the temperature s.ns.rs.and relative humidity s.ns.rs. This.s.udy provided a protocol for developing the inexpens.ve multi-s.ns.r s.ns.r node and s.owed that automated meas.rements.obtained with the s.s.em could help monitor variation in a greenhous. s.tting. The cos.s.of the s.s.em components.fabricated for this.s.udy included Us.76 for each s.ns.r node and Us.55 for the gateway, totaling Us.967 for the 12-node s.udy des.ribed.

关键词： Arduino Microcontroller LoRa Radio Temperature Humidity Light Level Clus.ering Heatmaps

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s.atial Analys.s.of s.ybean Plant Height and Plant Canopy Temperature Meas.red with On-the-Go Tractor Mounted s.ns.rs

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Agricultural s.iences 2019年第11期10卷 1486-1496页

作者： reginald s. fletcher Daniel K. Fis.er Crop Production s.s.ems.Res.arch Unit Agricultural Research Service United States Department of Agriculture Stoneville USA s.s.ainable Water Management Res.arch Unit Agricultural Research Service United States Department of Agriculture Stoneville USA

There is.a growing interes. in the Open Ag community to us. inexpens.ve s.ns.rs.controlled by open-s.urce s.ftware to meas.re plant height and plant canopy temperature of agricultural crops. Plant height and plant canopy temperature are key indicators.of plant health. This.res.arch s.udy reports.on an ongoing res.arch initiative to tes. a compact and inexpens.ve mobile s.ns.r to meas.re plant height and plant canopy temperature. The s.s.em is.controlled by open s.urce s.ftware and hardware. The s.ecific objectives.for this.s.udy were to analyze the relations.ip between plant height and plant canopy temperature of s.ybeans.(Glycine max L.) meas.red with the mobile s.s.em and to analyze the s.atial correlation of the plant height and plant canopy temperature meas.rements. Data were collected in a s.ybean plot in 2018 and 2019. Des.riptive s.atis.ics. Pears.n correlation, and geos.atis.ical techniques.were us.d to evaluate the data. A negative s.atis.ically s.gnificant (p ≤ 0.05) relations.ip was.obs.rved between the plant height and the plant canopy temperature meas.rements.(r = −0.54, 2018;r = −0.37, 2019). Als., both parameters.were s.atially correlated;however, plant height had a greater s.atial continuity than plant canopy temperature. Furthermore, s.milar patterns.were obs.rved for the in-field variability of the plant height and plant temperature maps.derived via kriging. s.milarities.in plant height and plant canopy temperatures.were obs.rved from one year to the next, s.gges.ing that the s.ns.r technologies.could be us.d as.a his.orical record for monitoring growth patterns.in s.ybean fields. The s.ns.rs.and techniques.us.d in this.s.udy can be eas.ly adapted to other crops. thus.providing two important layers.for monitoring plant growth and potentially plant s.res..

关键词： Glycine max Ultras.nic s.ns.rs Infrared Thermometer Correlation In-Field Variability

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A Miniature s.ns.r for Meas.ring Reflectance, Relative Humidity, and Temperature: A Greenhous. Example

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Agricultural s.iences 2018年第11期9卷 1516-1527页

作者： reginald s. fletcher Daniel K. Fis.er Crop Production s.s.ems.Res.arch Unit Agricultural Research Service United States Department of Agriculture Stoneville USA

There is.a growing interes. in us.ng miniature multi-s.ns.r technology to monitor plant, s.il, and environmental conditions.in greenhous.s.and in field s.ttings. The objectives.of this.s.udy were to build a s.all multi-channel s.ns.ng s.s.em with ability to meas.re vis.ble and near infrared light reflectance, relative humidity, and temperature, to tes. the light reflectance s.ns.rs.for meas.ring s.ectral characteris.ics.of plant leaves.and s.illes. media, and to compare res.lts.of the relative humidity and temperature s.ns.rs.to identical meas.rement obtained from a greenhous. s.ns.r. The s.ns.ng s.s.em was.built with off-the-s.elf miniature multis.ectral s.ectrometers.and relative humidity and temperature s.ns.rs. The s.ectrometers.were s.ns.tive to vis.ble, red-edge, and near infrared light. The s.s.em was.placed in a greenhous. s.tting and us.d to obtain relative reflectance meas.rements.of plant leaves.and s.illes. media and to record temperature and relative humidity conditions.in the greenhous.. The s.ectrometer data obtained from plant leaf and s.illes. media were compatible with bas.line s.ectral data collected with a hypers.ectral s.ectroradiometer. The greenhous. was.equipped with a relative humidity and temperature s.ns.r. The relative humidity and temperature s.ns.r meas.rements.from our s.ns.r s.s.em were s.rongly correlated with the relative humidity and temperature res.lts.obtained with the greenhous. s.ns.rs...

关键词： Arduino Kale Red Rus.ian Relative Humidity s.illes. Media s.ectrometer Temperature

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