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Plant Phenomics is a peer-reviewed journal published by AAAS (Science), covering Smart Agriculture and AI, Remote Sensing in Agriculture, Spectroscopy and Chemometric Analyses.
| Publisher | AAAS (Science) |
|---|---|
| Reference style | Superscript numbered (Science) Superscript — small raised numerals in the text 1. A. Smith, B. Jones, C. Lee, A representative article title. Plant Phenomics 12, 45–58 (2023).
Formats any DOI in Plant Phenomics style. No sign-up. |
| Publishes research in | Smart Agriculture and AI Remote Sensing in Agriculture Spectroscopy and Chemometric Analyses Remote Sensing and LiDAR Applications Leaf Properties and Growth Measurement |
| ISSN | 2097-0374 |
| Citation impact (2-yr) | 6.89 |
| h-index | 50 |
| i10-index | 254 |
| Total citations | 10,187 |
| Article processing charge | $2,500 |
| Open access | Yes |
| Top institutions publishing here | Nanjing Agricultural University |
| Journal website | spj.sciencemag.org |
| 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.
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has…
A plant develops the dynamic phenotypes from the interaction of the plant with the environment. Understanding these processes that span plant's lifetime in a permanently changing environment is essential for the advancement of basic plant science and its translation into application including breeding and crop management. The plant research community was thus confronted with the…
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these…
Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but…
The yield of cereal crops such as sorghum ( Sorghum bicolor L. Moench) depends on the distribution of crop-heads in varying branching arrangements. Therefore, counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field. However, measuring such phenotypic traits manually is an…
