Assessment of grass lodging using texture and canopy height distribution features derived from UAV visual-band images

Abstract

Lodging is a major limiting factor for the yield, quality and harvesting efficiency of selected crops worldwide. This study presents an efficient, robust and non-destructive assessment of lodging severity for four different grasses for seed production, using images collected by an unoccupied aerial vehicle (UAV) in two field plot experiments across five growing seasons. Canopy texture and height related features were extracted from individual plot images and evaluated for estimating lodging severity. Histograms of oriented gradients (HOG) were used as texture features, and three canopy height distributions features (CHV1, CHV2 and CHV3) were proposed. Each canopy height distribution feature divides the plots into subplots and estimates the average height of each subplot. CHV1 concatenates average height of the subplots into its feature, while CHV2 concatenates the difference in average height between all subplots, and CHV3 concatenates the difference in average height between adjacent subplots. The plots were classified using support vector machines into three categories according to the lodging severity. The results showed that the HOG and height distribution features can be used for grading lodging severity in UAV images with high accuracy (71.9% and 79.1%, respectively). However, the HOG features showed a negative relationship to the ground sample distance (GSD), while the CHV1 had a constant accuracy across the GSDs. Combination of the two features did not significantly improve the classification accuracy. The present results have potential to generate lodging severity maps for application in precision farming and thereby to increase grass seed yield and harvest efficiency at farm scale. It should be noted that results and methods from the current study might not be transferred to other crops due to crop specific lodging characteristics and effect of yields.

Plant phenotyping relevance

UAV画像から作物キャノピーのテクスチャ・高さ特徴を抽出し、倒伏重症度という植物状態を推定する手法を開発・評価しており、フェノタイピング手法が中心です。

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