An Overview on Phenomics Applications in Different Agriculture Disciplines

Abstract

In the past ten years, ground and aerial platforms with several sensors have been rapidly adopted for phenotyping diverse biotic, abiotic stressors and other characters during the crop plant's growth stages. An increase in yield achieved from traditional breeding programs worldwide are no longer sufficient to meet the projected demand for the three major cereals like Rice, Wheat and Maize. Thus the phenomics application which includes high throughput phenotyping (HTP) i.e artificial intelligence based techniques should be bought over traditional phenotyping in order to meet the needy demands of traditional ones. Also Artificial intelligence (AI)-based data analysis techniques can increase the reliability of diagnoses and, as a result, be included into instruments for effective treatment. These methods find pertinent data for use in plant breeding and pathology activities by using feature extraction, identification, classification, and prediction criteria as well as for precision breeding. This approach has various applications in various agriculture disciplines. The main steps under such techniques includes: Image acquisition, Preprocessing of images, segmentation of images, Image Representation and Description and Image recognition. The use of these helps to speed up genetic progress and lessen the phenotyping drawbacks.

Plant phenotyping relevance

植物フェノミクスおよび高スループット表現型解析のセンサー、画像取得・処理、特徴抽出、AI分析を農業分野横断で概説するレビューであり、方法論が中心です。

Code and data availability

This is a short review/overview article on phenomics applications. It describes methods and cites prior studies but presents no original phenotype datasets, images, code, models, or supplements; no public paper-specific asset is available.

No evidence-backed public reproduction asset is currently recorded.