AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

Abstract

Abstract Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. Here, we report AirSurf- Lettuce , an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. Utilising ultra-large normalized difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers’ precise harvest strategies and marketability estimates before the harvest.

Plant phenotyping relevance

レタスのサイズ分布や収量関連形質を航空画像から抽出するオープンソース解析プラットフォームが研究の中心であり、植物表現型計測手法に該当する。

Code and data availability

The supplied blocks describe the AirSurf-Lettuce platform, its CNN model, and NDVI imagery, and reference a GitHub repository and supporting files (Additional Files 1-3), but no explicit public URL or repository identifier for the code, model, or data appears in the supplied text. The only allowed URL is the preprint's

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