Computer vision-based plants phenotyping: A comprehensive survey

Abstract

The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.

Keywords: Machine learning; Phenotyping; Plant Biology.

Graphical abstract

Figure 1

Systematic diagram of the presented survey with basic image processing steps of recognition including image collection methods

Figure 2

The challenges acquired on different organs of plants (above-ground) using machine learning

Figure 3

Thresholding-based image segmentation methods

Figure 4

Clustering-based image segmentation methods

Figure 5

Deep Learning-based image segmentation methods