Browsing by Author "Okyere, Frank Gyan"

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    Computer vision methods for the analysis of multimodal and multidimensional data for high-throughput plant phenotyping
    (Cranfield University, 2023-12) Okyere, Frank Gyan; Mohareb, Fady R.; Hawkesford, Malcom J.; Simms, Daniel M.
    Drought and nutrient stresses substantially impact crop productivity, frequently resulting in lower yields and financial losses to farmers. Early detection and tracking of these stresses are significant for improving agricultural yield and achieving the world food challenge goals. Advanced image technologies such as the use of conventional cameras and imaging spectroscopy combined with computer vision methodolgoies are being leveraged for non-invasive plant phenotyping to improve crop productivity, resilience, and sustainability. This thesis seeks to combine these technologies with machine learning algorithms to develop automated phenotyping pipelines to phenotype plants at different growth stages, particularly; for nutrient and drought stress identification and quantification. With this proposal, the onset of plant nutrient and drought stresses could be detected early and at different growth stages of the plants. Three experiments (two in the glasshouse and one in the field) were conducted, and images were acquired using digital RGB and a hyperspectral camera. The first experiment (nutrient stress) was performed in the glasshouse, the second (nutrient stress) on the field and the third (drought stress) in the glasshouse. The glasshouse nutrient experiment was performed on quinoa and cowpea plants made of four treatments: high nitrogen high phosphorus (HNHP), high nitrogen low phosphorus (HNLP), low nitrogen low phosphorus (LNHP) and low nitrogen low phosphorus (LNLP). The field nutrient experiment was performed on wheat plants made of 12 Olsen phosphorus varitions (approximately 3, 6, 9, 12, 15, 18, 21, 25, 30, 40, 50 and 60 ppm). The third experiment is a wheat drought analysis under variable nitrogen at selected plant growth stages. The treatments include: well-watered high- nitrogen (WWHN), well-watered low-nitrogen (WWLN), drought-stress high-nitrogen (DSHN) and drought-stress low-nitrogen (DSLN). Several image processing and machine learning techniques were employed to pre-process, post-process, and analyze plant- specific traits for tracking plant drought and nutrient stresses. Specifically, using digital imaging, a new segmentation algorithm invariant to illumination and complex background scenes was proposed to segment field and glasshouse-based images. Statistical and machine learning methods were employed to identify phenotypic traits sensitive to nutrient (nitrogen and phosphorus) deficiencies. Additionally, plant colour, morphology, and texture features were critically analyzed to assess their response to different stresses in plants. Using a hyperspectral imaging, a hybrid deep learning model was proposed to detect and track plant nitrogen and phosphorus deficiencies. In this case , the spatial and spectral characteristics of plants were analyzed, and deep learning algorithms were combined to understand their response to nutrient and drought stress in plants. Finally, using the spectral characteristics of plants, different conventional machine learning algorithms including Random Forest, Partial Least Square Regression and Support Vector Machines were developed to model the trends and patterns of plants for drought stress detection. The research results show a link between colour and nutrient stress, while texture and colour features were highly responsive to drought stress. The short-wave infrared region of the electromagnetic spectrum was highly responsive to plant phosphorus deficiency, while the blue, red, and near-infrared regions were highly connected to plant nitrogen deficiency. Furthermore, combining a proposed vegetation indices (VIs) from the VNIR regions of the spectrum with already known VIs resulted in the easy identification of plant drought stress compared to using only the known or proposed indices.
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    Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat
    (MDPI, 2024-09-17) Okyere, Frank Gyan; Cudjoe, Daniel Kingsley; Virlet, Nicolas; Castle, March; Riche, Andrew Bernard; Greche, Latifa; Mohareb, Fady; Simms, Daniel; Mhada, Manal; Hawkesford, Malcolm John
    Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.
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    Machine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotyping
    (MDPI, 2023-05-19) Okyere, Frank Gyan; Cudjoe, Daniel M.; Sadeghi-Tehran, Pouria; Virlet, Nicolas; Riche, Andrew B.; Castle, March; Greche, Latifa; Mohare, Fady; Simms, Daniel M.; Mhada, Manal; Hawkesford, Malcolm John
    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.
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    Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods
    (Frontiers, 2023-10-16) Okyere, Frank Gyan; Cudjoe, Daniel; Sadeghi-Tehran, Pouria; Virlet, Nicolas; Riche, Andrew B.; Castle, March; Greche, Latifa; Simms, Daniel M.; Mhada, Manal; Mohareb, Fady; Hawkesford, Malcolm John
    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.