Cabbage plant (Brassica oleracea var. capitata L.) quantification culti-vated under different soil covers using aerial photographs

Abstract

var. capitata L.) quantification cultivated under different types of mulching, using aerial images captured by RPAS (Remotely Piloted Aircraft System). Design/methodology/approach: The cabbage plantation used for the study was established under a completely randomized block design with different types of mulch as treatments: black plastic, white plastic, straw, and bare soil. Manual plant counts and automated estimates were performed using two agricultural artificial intelligence platforms (Platforms A and B). The relationship was evaluated using linear regression correlation (R²), and the following indicators were subsequently used: estimation accuracy (Ps), estimation error percentage (Es), mean absolute error (MAE), and root mean square error (RMSE). Results: Platform A showed a correlation coefficient range of R²=0.41 to 0.91. Platform B obtained R² values ranging from 0.77 to 0.88. Platform A exhibited the highest estimation accuracy (Ps) with 98.3% and an estimation error (Es) of -1.7% for straw mulch, with a mean absolute error (MAE) of 2.0% and a root mean square error (RMSE) of 1 for bare soil. Both platforms showed underestimations in the number of detected plants, ranging from -6.7% to -1.7%. Limitations on study/implications: The use of RPAS was limited by atmospheric conditions such as wind and rain. Findings/conclusions: The effectiveness of counting cabbage plants using RPAS was validated.

Plant phenotyping relevance

RPAS画像とAIプラットフォームによるキャベツ個体数の自動推定を開発・評価し、手動計数との相関や誤差で検証しているため、植物表現型取得手法が中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.