About the Journal

The Plant Phenome Journal is a transdisciplinary, gold open access journal that focuses on original research, interpretations, and datasets investigating all aspects of plant phenomics. TPPJ is a forum that combines engineering, remote sensing, computer or data science with plant science, breeding, genetics, or agronomy.

Featured Article

Spectral analysis tool flowchart
Making waves in Breedbase: An integrated spectral data storage and analysis pipeline for plant breeding programs

Looking to add visible or near-infrared spectroscopy to your plant breeding toolbelt? Try out waves, a new rstats and Breedbase tool for the analysis of spectral data in plant breeding and genetics. Hershberger et al. developed waves, which is compatible with a wide range of spectrometer models and performs visualization, filtering, aggregation, cross-validation set formation, model training, and prediction functions for the association of vis-NIRS spectra with reference measurements...Read more

Browse Articles

Open access

Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems

Core Ideas

  • Four ground control points are sufficient to georeference aerial photogrammetry projects.
  • Real-time kinematic positioning data can be used instead of ground control points (GCPs) to georeference projects.
  • Using GCPs and real-time kinematic positioning together results in highly accurate products.
  • Popular photogrammetry software perform similarly and can each be used for agriculture.

Open access

Prospector: A mobile application for portable, high‐throughput near‐infrared spectroscopy phenotyping

Core Ideas

  • Prospector is a mobile application for the streamlined collection of near-infrared spectral data.
  • Barcoded samples increase the efficiency of NIRS data collection.
  • Prospector connects to both models of LinkSquare spectrometers.
  • Data can be exported as organized CSV text files for subsequent analyses.

Open access

Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants

Core Ideas

  • Latent phenotyping is an emerging field that captures fine scale variation in plants.
  • Latent phenotypes can minimize the effect of human bias when describing plant form from high dimensional data.
  • Predictions and inferences from latent phenotyping are useful for crop improvement and basic biology.

Open access

Automation of leaf counting in maize and sorghum using deep learning

Core Ideas

  • Automated leaf counting in maize was previously limited by annotated training data.
  • Two deep learning approaches both achieve near human accuracy in counting leaves.
  • There is an ongoing need to extend image datasets with phenotypically extreme plants.

Open access

Measuring canopy height in soybean and wheat using a low‐cost depth camera

Core Ideas

  • The author planned to determine if the D415 camera is suitable for measuring canopy height in a field setting.
  • The manuscript examines the ease of incorporating the D415 into a mobile ground-based phenomics platform.
  • The proposed goal of incorporating the D415 depth camera was to increase the speed and accuracy of height data collection in field trials.

Open access

Temporal Estimates of Crop Growth in Sorghum and Maize Breeding Enabled by Unmanned Aerial Systems

Abstract

Core Ideas

  • We comprehensively validated the use of UAS in sorghum and maize breeding programs.
  • Temporal estimates of plant growth will allow researchers to elucidate new phenotypes.
  • The stage of the breeding pipeline dictates the applicability of UAS platforms.
  • The implementation of UAS is demonstrated in different crop species.
  • Monetary and time costs should be considered before implementation of UAS.

To meet future world food and fiber demands, plant breeders must increase the rate of genetic improvement of important agricultural crops. One of the biggest obstacles now facing crop scientists is a phenotyping bottleneck. To ease this burden, the emerging technology of unmanned aerial systems (UAS) presents an exciting opportunity. To assess the utility of UAS, it is important to investigate their application across multiple crop species. Terminal plant height is of great importance to maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] breeders and has been hypothesized to be useful but has been logistically impractical to measure in the field. In this study, we statistically analyzed in depth the ability of UAS to estimate height in sorghum (advanced and early generation material) and maize (optimal and late material) and the application of these estimates in breeding programs. We found that UAS explain genotypic variation similarly to ground-truth methods and that the repeatability of the methodology is high (R = 0.61–0.99), indicating effective differentiation of genotypes. Additionally, correlations between ground truth and UAS measurements were moderate to high for all materials (r = 0.4–0.9). Finally, we present a novel application for the technology in the form of high-resolution temporal growth curves. Using these UAS-generated growth curves, new physiological insights can be obtained and new avenues of scientific investigation are possible.

Open access

Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Abstract

    Core Ideas
  • UAS captured increased genetic variation compared with manual terminal height.
  • There were small significant differences in ground filtering methods to extract plant structure.
  • Higher resolution did not improve imagery informativeness with regard to plant height.
  • Logistic function provides informative phenotypes for temporal maize growth.
  • Correlation and prediction accuracy of grain yield increased by ∼20% with UAS heights.

Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across three environmental stress treatments, using two UAS platforms. The high-altitude (120-m) fixed-wing platform increased the fraction of variation attributed to genetics and had highly repeatable (R > 60%) height estimates, increasing the genetic variance explained (10–40%) over traditional terminal plant height measurement (PHTTRML ∼30%), as well as over the low-altitude rotary-wing UAS platform (10–20%). A logistic function reduced the dimensionality (>20 flights) of each UAS dataset to three parameters (inflection point, growth rate, and asymptote) and produced a more robust predictive model than independent flight dates, effectively summarizing (R2 > 0.98) the UAS flight dates. The logistic model overcame the need to use specific flight dates when comparing different environments. The UAS height estimates (r = 0.36–0.48) doubled the correlations to grain yield in this G2F experiment compared with PHTTRML (r = 0.23–0.28). Parameters of the logistical function achieved equivalent correlations (r = 0.30–0.46) to individual flight dates (r = 0.36–0.48), improving grain yield prediction by ∼400% (R2 = 0.25–0.34) over PHTTRML (R2 = 0.06–0.08). Incorporating other UAS-derived parameters beyond plant height may allow yield to be accurately predicted before maturity, speeding breeding programs. A new public R function to generate ESRI shapefiles for plot research is also described.

Open access

Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery

Abstract

    Core Ideas
  • A deep learning model identified plant disease in UAV images with 95% accuracy.
  • Transfer learning allowed for faster model optimization.
  • This method detected plant disease symptoms at a very fine spatial scale.

The detection, diagnosis and quantification of plant diseases using digital technologies is an important research frontier. New and accurate methods would be an asset to growers, for whom early disease detection can mean the difference between successful intervention and massive losses, and plant breeders, who often must rely on time-consuming phenotyping by eye. We have developed such a method for detecting an important maize (Zea mays L.) disease. Northern leaf blight [NLB; causal agent Setosphaeria turcica (Luttrell) Leonard & Suggs] is a foliar disease of maize that causes significant yield losses. Accurately measuring NLB infection is necessary both for breeding more resistant maize lines and for guiding crop management decisions. Visual disease scoring in a large area is time-consuming and human evaluations are subjective and prone to error. In this work, we demonstrate an automated, high-throughput system for the detection of NLB in field images of maize plants. Through the use of an unmanned aerial vehicle (UAV) to acquire high resolution images, we trained a convolutional neural network (CNN) model on lower resolution sub-images, achieving 95.1% accuracy on a separate test set of sub-images. The CNN model was used to create interpretable heat maps of the original images, indicating the locations of putative lesions. Detecting lesions at a fine spatial scale allows for the potential of unprecedented high-resolution disease detection for plant breeding and crop management strategies.

Open access

Big Data Driven Agriculture: Big Data Analytics in Plant Breeding, Genomics, and the Use of Remote Sensing Technologies to Advance Crop Productivity

Abstract

Core Ideas

  • Interdisciplinary efforts in high-throughput field phenotyping
  • Linking proximal and remote field phenotyping
  • Cyberinfrastructure for high-throughput field phenotyping

Plant breeding and agronomy are labor-intensive sciences, and the success of these disciplines is critical to meet planetary challenges of food and water security for the world's growing population. Recent gains in sensor technology, remote sensing, robotics and autonomy, big data analytics, and genomics are being adopted by agricultural scientists for high-throughput phenotyping, precision agriculture, and crop-scouting platforms. These technological gains are ushering in an era of digital agriculture that should greatly enhance the capacity of plant breeders and agronomists. This report encompasses the priorities and recommendations that emerged from two USDA National Institute of Food and Agriculture (NIFA)-funded Big Data Driven Agriculture workshops held on 26–27 Feb. 2018 in Arlington, VA. The objectives of the workshops were to bring together diverse subject-matter experts in the represented disciplines of plant breeding, machine learning, remote sensing, and big data infrastructure and analytics to (i) explore how large and comprehensive datasets in plant breeding, genomics, remote sensing, and analytics will benefit agriculture; (ii) discuss strategies for creating a successful field phenotyping campaign and to determine protocols for the collection and analysis of agricultural big data; (iii) consider how to best engage the broader community of public and private plant breeders and agronomists to determine additional challenges, make wider use of the data, and ensure application of standardized methods to other datasets; and (iv) generate a report describing cross-cutting short- and long-term funding needs for continued success in this domain.

Open access

FIELDimageR: An R package to analyze orthomosaic images from agricultural field trials

Abstract

Remote sensing is revolutionizing the phenotyping of agricultural field trials, but for many researchers, the extraction of plot-level results is a bottleneck. We have developed the R package FIELDimageR as a user-friendly tool to analyze orthomosaic images containing many plots. The basic workflow involves cropping and rotating the image, followed by the creation of a shapefile based on the experimental design. The package includes functions to calculate the number of plants per plot, canopy cover percentage, vegetation indices, and plant height. FIELDimageR is publicly available as a GitHub repository (https://github.com/filipematias23/FIELDimageR).

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