Advances in Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.

The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.

In the present book, nine typical literatures about plant phenomics published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on plant phenomics. We hope this book can demonstrate advances in plant phenomics as well as give references to the researchers, students and other related people.

Sample Chapter(s)
Preface (87 KB)
Components of the Book:
  • Chapter 1
    Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks
  • Chapter 2
    Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting
  • Chapter 3
    Classification of Plant Endogenous States Using Machine Learning-derived Agricultural Indices
  • Chapter 4
    A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background
  • Chapter 5
    High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants
  • Chapter 6
    Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images
  • Chapter 7
    Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders
  • Chapter 8
    Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping
  • Chapter 9
    Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns
Readership: Students, academics, teachers and other people attending or interested in Plant Phenomics
Giuseppe Montanaro
University of Basilicata, Potenza, Basilicata, ITALY

Ian Stavness
Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

Karine Chenu
The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Toowoomba, QLD, Australia

Scott C. Chapman
School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia

Patrick S. Schnable
Plant Sciences Institute, Iowa State University, Ames, IA, USA

and more...
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