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1.
Front Plant Sci ; 14: 1209500, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37908836

RESUMO

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

2.
Plants (Basel) ; 12(10)2023 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-37653952

RESUMO

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

3.
Sci Total Environ ; 849: 157826, 2022 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-35932859

RESUMO

Rivers are dynamic landscape features that change in response to natural and anthropogenic factors through hydrological, geomorphic and ecological processes. The severity and magnitude of human impacts on river system and riparian vegetation has dramatically increased over the last century with the proliferation of valley-spanning dams, intensification of agriculture, urbanization, and more widespread channel engineering. This study aims to determine how changes in geomorphic form and dynamics caused by these human alterations relate to changes in channels and riparian vegetation in the lower Beas and Sutlej Rivers. These rivers are tributaries of the Indus that drain the Western Himalayas but differ in the type and magnitude of geomorphic change in recent decades. Winter season vegetation was analysed over 30 years, revealing increasing trends in vegetated land cover in the valleys of both rivers, consistent with large-scale drivers of change. Greater trends within the active channels indicate upstream drivers are influencing river flow and geomorphology, vegetation growth and human exploitation. The spatial patterns of vegetation change differ between the rivers, emphasizing how upstream human activities (dams and abstraction) control geomorphic and vegetation community response within the landscape context of the river. The increasing area of vegetated land is reinforcing the local evolutionary trajectory of the river planform from wide-braided wandering to single thread meandering. Narrowing of the active channels is altering the balance of resource provision and risk exposure to people. New areas being exploited for agriculture are exposed to greater risk from river erosion, inundation, and sediment deposition. Moreover, the change from braided to meandering planform has concentrated erosion on riverbanks, placing communities and infrastructure at risk. By quantifying and evaluating the spatial variations in vegetation cover around these rivers, we can better understand the interaction of vegetation and geomorphology alongside the impacts of human activity and climate change in these, and many similar, large systems, which can inform sustainable development.


Assuntos
Hidrologia , Rios , Agricultura , Mudança Climática , Ecossistema , Humanos , Urbanização
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