Advancing biomass estimation in hydroponic lettuce using RGB-depth imaging and morphometric descriptors with machine learning

Abstract

By capturing the intricate structural and spectral variations of the plant canopy, we can enhance our ability to model and predict dynamic parameters such as biomass with greater precision. This method not only preserves the plants for continuous monitoring but also provides a scalable and efficient alternative to traditional destructive techniques. The objective of this study was to examine the potential of using image-derived color and geometric plant features to output accurate predictions of three plant biomass accumulation parameters − leaf fresh weight, leaf dry weight, and leaf area for single plant monitoring. Top-view images of a hydroponic ‘Chicarita’ romaine lettuce (Lactuca sativa) crop captured with a color and depth sensor were used as the input of a multiple plants image processing workflow that extracted plant height, canopy morphometric, and color traits at an individual plant level. Two destructive harvest rounds were performed across the plant cycle to measure the observed values for each biomass response given by leaf fresh weight, leaf dry weight and leaf area from two crop cycles. The image-derived traits were used as potential predictors for a simple linear regression used as a baseline model and for two supervised machine learning models (random forest and least absolute shrinkage and selection operator or LASSO regression) to estimate each response. Using extracted depth information, vertical height per plant was estimated with a mean absolute error of 1.51 cm. Random Forest regression models yielded the most accurate predictions on a first harvest round for all three biomass parameters with R² values of 0.74, 0.80, and 0.67 and mean absolute percentage error (MAPE) of 11.77%, 10.16%, and 12.50%. LASSO regression outperformed the other models in a second harvest round with R² values of 0.72, 0.65, and 0.79 and MAPE of 7.79%, 7.76%, and 7.06% for leaf fresh weight, leaf dry weight, and leaf area, respectively. These results suggest that using a selection of canopy descriptors may improve the non-destructive biomass estimation along a lettuce crop cycle, enabling remote monitoring and real-time harvest projections.

Plant phenotyping relevance

RGB-depth画像から植物形質を抽出し、機械学習でレタスのバイオマスを非破壊推定する手法が研究の中心であるため。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.