This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in two campaigns (June–November 2017 and January–March 2018), on a private farm, TESKO, located in Juan Hombrón, Coclé Province, Panama. The spectral fingerprint of IDIAP 52-05 plants was collected in four dates (47, 67, 82 and 116 days after sowing), according to known phenological stages of rice plant growth. Moreover, true LAI or green leaf area was measured from representative plants and compared to LAI calculated from normalized PlanetScope multi-spectral satellite images (selected according to dates close to the in-field collection). Two distinct estimation models were used to establish the relationships of measured LAI and two vegetational spectral indices (NDVI and MTVI2). The results show that the MTVI2 based model has a slightly higher predictive ability of true LAI (R2 = 0.92, RMSE = 2.20), than the NDVI model. Furthermore, the satellite images collected were corrected and satellite LAI was contrasted with true LAI, achieving in average 18% for Model 2 for MTVI2, with the NDVI (Model 1) corrected model having a smaller error around 13%. This work provides an important advance in precision agriculture, specifically in the monitoring of total crop growth via LAI for rice crops in the Republic of Panama.
In this article supervised classification methods for the analysis of local Panamanian rice crops using Near-Infrared (NIR) spectral signatures are assessed. Neural network (Multilayer Perceptron-MLP) and Tree based (Decision Trees-DT and Random Forest-RF) algorithms are used as regression and supervised classification of the spectral signatures by rice varieties, against other crops and by plant phenology (days after planting). Also, satellite derived spectral signature is validated with a field collected spectral model. Results suggest that MLP networks, either for regression or classification, were more efficient (RMSE of 8.78 and 0.068, respectively) than either tree based methods to regress/classify the rice spectral signature (RMSE of 19.37,19.09 and 0.979, respectively). The validation made using satellite derived spectral signatures resulted in MLP models with RMSE of 0.216 and 7.318, respectively, leaving room for further improvement of the models. This work aimed to present a practical example of the employment of recent supervised classification algorithms for the determination of regression and classification models from reflectance spectral signatures in local rice varieties.
The present work is part of the project SENACYT IDDS 15-184 titled "Designing an expert system based on spectral signatures of agricultural coverage in Panama". The study site is located in the Pacific Sub-center Marciaga of the Institute of Agricultural Research of Panama (IDIAP), in El Coco de Penonomé, province of Coclé. In This sub-center has plots cultivated with varieties of rice, including creole "criollo" varieties for the conservation of genetic diversity A spectral library of crops was created of rice. The varietal description of 6 cultivars is presented criollos along with the analysis of their spectral signatures.
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