Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods.

Abstract

Leaf area index (LAI) is a key indicator of crop growth and development and is widely used in both agricultural research and precision farming applications. PlanetScope imagery is generally used for monitoring crop growth due to its high revisit frequency, broad spatial coverage, and cost-effective access to consistent high-resolution multispectral data. Therefore, we developed regression models to estimate peanut LAI, combining PlanetScope spectral bands and vegetation indices (VIs). Specifically, we compared the performance of random forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) regression algorithms for peanut LAI estimation. Our results showed that most of the VIs exhibited strong relationships with LAI. Thirteen VIs were individually evaluated for estimating LAI using the aforementioned algorithms, and our results showed that the best single predictors of LAI are: TSAVI (RF: R 2 = 0.87, RMSE = 0.83 m 2 /m 2 , RRMSE = 24.20%; XGBoost: R 2 = 0.77, RMSE = 0.95 m 2 /m 2 , RRMSE = 27.96%); and RTVIcore (PLSR: R 2 = 0.68, RMSE = 1.12 m 2 /m 2 , RRMSE = 32.88%). The top six ranked VIs were used to calibrate the RF, XGBoost, and PLSR algorithms. Model validation indicated that RF achieved the highest accuracy (R 2 = 0.844, RMSE = 0.858 m 2 /m 2 , RRMSE = 25.17%), followed by XGBoost (R 2 = 0.808, RMSE = 0.92 m 2 /m 2 , RRMSE = 26.99%), whereas PLSR showed comparatively lower performance (R 2 = 0.76, RMSE = 0.983 m 2 /m 2 , RRMSE = 28.85%). Further results showed that PlanetScope VIs provided superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Additionally, integrating spectral bands with VIs reduced LAI estimation accuracy, underscoring the importance of selecting predictor variables in ensuring optimal model performance. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI estimation.

Plant phenotyping relevance

PlanetScopeスペクトルデータと複数の回帰手法により、ピーナッツのLAIという植物形質を推定し、モデル性能を比較・検証している。形質取得・推定手法が研究の中心である。

Code and data availability

The supplied blocks describe peanut LAI measurements (83 in situ VitiCanopy points) and PlanetScope imagery, but no public dataset deposit, author code repository, trained model checkpoint, or supplement with data/code is mentioned. The only URL (Planet Explorer) is a generic commercial imagery portal, not a paper-phen

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