POTENTIAL OF MULTISPECTRAL IMAGES TAKEN BY SENSORS EMBEDDED IN UAVS FOR MONITORING THE COFFEE CROP IRRIGATION

Abstract

Abstract. Leaf Water Potential (LWP) is an indicator widely used to understand water relations in a coffee tree. Monitoring water potential is a challenge for remote sensing using low-cost multispectral cameras, with images taken by remotely piloted aircraft. The objective of this work was to evaluate the potential of a low-cost camera to discriminate different water treatments in the coffee tree. In addition, the accuracy of models to estimate LWP in the coffee crop was evaluated. The results showed that the NDVI (Normalized Difference Vegetation Index) vegetation index was able to discriminate 61.6 % more plots in a drought regime than the Near-InfraRed (NIR) band in the rainfall regime. For LWP, the architecture that presented the best performance in the detection of water stress was for the first flight (SMOreg algorithm using as predictor variables all bands, Red, Green, and NIR, and the NDVI vegetation index) with RMSE value of 0.1880 and RMSE% of 34.18. For the second flight (Random Tree algorithm, using as predictor variables all bands and NDVI) with RMSE (0.0520) and RMSE% (32.00) values.

Plant phenotyping relevance

UAVマルチスペクトル画像からコーヒー樹の葉水ポテンシャルと水ストレスを推定し、モデル精度を評価することが中心であり、植物生理状態の取得・推定手法を技術的に検証している。

Code and data availability

The paper describes UAV multispectral imaging and machine-learning models for coffee leaf water potential estimation, but contains no data availability statement, no public dataset or image deposit, and no author code/model release. The only URLs present are the article DOI and a CONAB statistics bulletin reference (a

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