Image-based plant phenotyping with incremental learning and active contours

• Published in: Ecological informatics Vol. 23; pp. 35 - 48
• Main Authors: Minervini, Massimo, Abdelsamea, Mohammed M., Tsaftaris, Sotirios A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2014; Elsevier
• Subjects: Active contour model; Agriculture; Agronomy. Soil science and plant productions; Animal, plant and microbial ecology; Arabidopsis; Biological and medical sciences; cameras; color; computer software; data collection; digital images; experimental design; Fundamental and applied biological sciences. Psychology; Gaussian mixture model; General agroecology; General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping; General agronomy. Plant production; General aspects. Techniques; Generalities. Agricultural and farming systems. Agricultural development; genome; image analysis; infrastructure; Machine learning; Methods and techniques (sampling, tagging, trapping, modelling...); phenotype; Phenotyping; Plant segmentation; sequence analysis; sustainable agriculture; texture; user interface; Active contour model; Agriculture; Gaussian mixture model; Phenotyping; Machine learning; Plant segmentation; Learning; Segmentation; Contour; Models; Image

Plant phenotyping investigates how a plant's genome, interacting with the environment, affects the observable traits of a plant (phenome). It is becoming increasingly important in our quest towards efficient and sustainable agriculture. While sequencing the genome is becoming increasingly effic...