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The Plant Phenome Journal is a peer-reviewed journal published by Wiley, covering Remote Sensing in Agriculture, Smart Agriculture and AI, Genetic Mapping and Diversity in Plants and Animals.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." The Plant Phenome Journal 12 (3): 45–58.
Formats any DOI in The Plant Phenome Journal style. No sign-up. |
| Publishes research in | Remote Sensing in Agriculture Smart Agriculture and AI Genetic Mapping and Diversity in Plants and Animals Spectroscopy and Chemometric Analyses Leaf Properties and Growth Measurement |
| ISSN | 2578-2703 |
| Citation impact (2-yr) | 4.31 |
| h-index | 27 |
| i10-index | 67 |
| Total citations | 2,609 |
| Article processing charge | $1,750 |
| Open access | Yes |
| Top institutions publishing here | Cornell University |
| Journal website | acsess.onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
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…
Abstract Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a…
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…
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…
Abstract High‐throughput phenotyping technologies, which can generate large volumes of data at low costs, may be used to indirectly predict yield. We explore this concept, using high‐throughput phenotype information from Fourier transformed near‐infrared reflectance spectroscopy (NIRS) of harvested kernels to predict parental grain yield in maize ( Zea mays L.), and demonstrate a proof of…
