This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.
The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.
A marcha de absorção estuda a relação existente entre a quantidade de nutrientes, o acúmulo de matéria seca e a idade da planta, permitindo identificar, a) quantidade de nutrientes necessários para a produção; b) época de maior exigência de cada nutriente; c) em qual ór-gão cada nutriente se encontra em maior quantidade; d) o quanto é exportado pela colheita e o quanto será necessário repor ao solo para não exauri-lo (Marschner, 1995). Portanto, a marcha de absorção das culturas é uma importante ferramenta para auxiliar os programas de adubação e o manejo de fertilizantes das lavouras. No entanto, no Brasil, informações sobre a absorção de nutrientes e a curva de acúmulo de massa seca na cultura da batata-doce são escassos ou inexistentes.A composição mineral nos tecidos foliares varia conforme a espécie, culti-ECHER FR; DOMINATO JC; CRESTE JE. 2009. Absorção de nutrientes e distribuição da massa fresca e seca entre órgãos de batata-doce.Horticultura Brasileira 27: 176-182. (Malavolta et al., 1997), e a absorção de nutrientes em hortaliças segue um padrão de crescimento ou acúmulo de massa seca, sendo o potássio normalmente o mais absorvido (Ferreira et al., 1990).A batata-doce é uma dicotiledônea da família Convolvulaceae, que possui alta eficiência na captação da energia solar e grande capacidade de produzir matéria seca por um longo período de tempo (Hahn, 1977). Seu caule é herbá-ceo de hábito prostrado, folhas largas e pecíolo longo (Embrapa, 2008). Possui dois tipos de raiz: a tuberosa, que constitui a principal parte de interesse comercial, e a raiz absorvente, responsá-vel pela absorção de água e extração de nutrientes do solo.Os mecanismos morfo-fisiológicos contribuem para o uso eficiente de nutrientes pelas plantas. Entre eles, estão o sistema radicular eficiente e extensivo; alta relação raiz/parte aérea; associação a microrganismos que fixam N 2 atmosférico; habilidade das raízes em modificar a rizosfera para superar situações de baixa disponibilidade de nutrientes; capacidade de manter o metabolismo inalterado com baixas concentrações de nutrientes nos tecidos e, alta taxa fotossintética (Fageria & Baligar, 1993).Por ter elevado potencial produtivo, a batata-doce requer grande quantidade de nutrientes. Para uma produção de 11 a 15 t ha -1 de raízes tuberosas, estimase que a extração seja de 60 a 113 kg de N, 20 a 47,5 kg de P 2 O 5 , 100 a 236 kg de K 2 O, 31 a 35 kg de CaO e 11 a 13 kg de MgO (Silva et al., 2002).No Brasil, dados referentes à extração de micronutrientes pela batata-doce são escassos. No entanto, relatos na literatura indicam que para cada tonelada RESUMO A marcha de absorção estuda a relação existente entre a quantidade de nutrientes, o acúmulo de massa seca e a idade da planta. O objetivo deste trabalho foi determinar a marcha de absorção de nutrientes e a produção de massa fresca e seca dos órgãos da batatadoce, cultivar Canadense. O experimento foi conduzido entre os meses de agosto e dezembro de 2007, em lavoura comercial, em Presidente Prudente-SP. O delineamento experimenta...
Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.
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