Multispectral images for estimating morphophysiological and nutritional parameters in cabbage seedlings

Martins, George Deroco; Santos, Ludymilla Célia Sousa; Carmo, Glecia Júnia dos Santos; Neto, Onésio Francisco da Silva; Castoldi, Renata; Charlo, Hamilton César de Oliveira

doi:10.1016/j.atech.2023.100211

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The extent of the area's vegetation loss is thus explained, particularly in light of the industrial zone's expansion. By using the infrared band of the electromagnetic spectrum, where vegetation reflects the most energy that can be observed and recorded by a satellite sensor (Berger et al, 2020;Joiner et al, 2013;Martins et al, 2023), the NDVI is used to display the chlorophyll activity of the vegetation. According to the NDVI an area with defensible and living vegetation has a high value, while one without vegetation has a low value.…”

Section: Methodsmentioning

confidence: 99%

Industrial Investing as an Instrument of Sustainable Development in Algeria (Case in Bouira Province - Eastern Algeria)

BELARBI,

BOUDER

2023

GTG

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Algeria has developed an interesting manufacturing sector since its independence with the aim to develop the national economy. In this paper, we'll highlight the industrial investments that Bouira's municipal government (Easter Algeria) made between 2003 and 2020. To achieve this goal, we used a series of LANDSAT 7ETM+/LANDSAT 8OLI satellite images acquired respectively in 2006, 2014, and 2020. The processing applied to images evaluates the normalized vegetation (NDVI), which provides an estimation of the used area for industrial investment. All processed images were combined in a GIS (Geographic Information System), to digitize subsequently industrial areas. This research shows that over 242.69 ha of Oued El-Berdi's zone was used for industrial projects.

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Section: Methodsmentioning

confidence: 99%

Industrial Investing as an Instrument of Sustainable Development in Algeria (Case in Bouira Province - Eastern Algeria)

BELARBI,

BOUDER

2023

GTG

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“…However, it is crucial to investigate whether UAV data can accurately reflect the growth status of maize crops. Previous studies have shown that VI features extracted from multispectral data can effectively characterize crop growth [34]. Additionally, LIDAR point cloud data have been utilized to describe changes in crop canopy structure [14,35].…”

Section: Correlation Analysis Between Uav Remote Sensing Data and Mai...mentioning

confidence: 99%

A Prediction Model of Maize Field Yield Based on the Fusion of Multitemporal and Multimodal UAV Data: A Case Study in Northeast China

et al. 2023

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The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75–100 in the later part of the V period, Point80–100 in the R period, and Point50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops.

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Estimation of macro and micronutrient concentrations in ‘valencia’ orange leaves at different phenological stages using multispectral image analysis and multiple regression

de Oliveira,

Da Silva-Junior,

Barron-Zambrano

et al. 2024

International Journal of Remote Sensing

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Multispectral images for estimating morphophysiological and nutritional parameters in cabbage seedlings

Cited by 3 publications

References 28 publications

Industrial Investing as an Instrument of Sustainable Development in Algeria (Case in Bouira Province - Eastern Algeria)

Industrial Investing as an Instrument of Sustainable Development in Algeria (Case in Bouira Province - Eastern Algeria)

A Prediction Model of Maize Field Yield Based on the Fusion of Multitemporal and Multimodal UAV Data: A Case Study in Northeast China

Estimation of macro and micronutrient concentrations in ‘valencia’ orange leaves at different phenological stages using multispectral image analysis and multiple regression

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