Phenotyping the nutritional status of crops using proximal and remote sensing techniques

Understanding the nutritional needs of crops is crucial for ensuring their health and maximising yield. However, the capability to accurately measure relevant physical characteristics (phenotypes) of important crops in response to complex nutrient stresses is limited. For crop breeders and researchers, the existing capacity to characterise crops with adequate precision, detail and efficiency is hindering significant progress in crop development. In this PhD thesis, the use of advanced sensing techniques to assess the nutritional status of African crops was explored, focusing on three main objectives. First, the use of a handheld proximal sensor was investigated to evaluate the spectral properties of quinoa and cowpea crops grown under different N and P supplies in controlled glasshouse conditions (Chapter 3). By analysing these spectral properties, the aim was to identify spectral indices that could show early signs of N and P stress separately in the plants. These stress indicators were related to the overall performance of the crops. Spectral indices were found that could distinguish between N and P stress at the early growth stage of the crops. However, identifying spectral indices for P stress was limited, particularly in cowpea due to the shorter wavelength range of the handheld device. The results showed significant relationships between the spectral indices and traits related to the morphology, physiology and agronomy of the crops. Second, it was demonstrated that different levels of N impact the drought responses of spring wheat (Chapter 4). By evaluating morpho-physiological changes in the plants under high N and low N conditions, an understanding of how spectral reflectance measured at the leaf level could help distinguish between combined and complex stresses such as drought and nutrient deficiency was investigated. The results showed a greater amplitude of drought response in plants that were supplied with high N compared to low N levels, with interactive effects on many morphological and physiological traits. Out of a group of 39 different SRIs, only the Renormalised Difference Vegetation Index (RDVI) and the Red Difference Vegetation Index (rDVI_790) showed better accuracy in detecting drought stress. The results also revealed that indices sensitive to chlorophyll levels, such as the chlorophyll Index (mNDblue_730), Greenness Index (G) and Lichtenthaler Index (Lic2), as well as red-edge indices like Modified Red-Edge Simple Ratio (MRESR), chlorophyll Index Red-Edge (CIrededge) and Normalised Difference Red-Edge (NDRE), were more accurate in detecting N stress. Lastly, the effectiveness of using spectral information from images collected from a drone and spectral reflectance measured with proximal sensors on the ground were compared for detecting N stress in winter wheat under field conditions (Chapter 5). By comparing these two sensing methods, it was assessed which approach is more accurate, reliable and cost- effective for assessing the N nutritional needs of the crop in real-world agricultural settings. The results indicated that the NDVI measured on the ground at the leaf level could accurately detect the small changes in N levels earlier compared to the drone NDVI and canopy level NDVI and for assessing the agronomic performance of winter wheat. Overall, this PhD research sheds new light on the potential of advanced sensing techniques to improve crop management practices and enhance agricultural productivity by providing timely and accurate information about the nutritional status of the studied crops.PhD in Environment and Agrifoo

Field phenotyping for African crops: overview and perspectives

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.BBSRC: BB/P016855/

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

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

Machine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotyping

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

Implementation of the World Health Organization's QualityRights initiative in Ghana: an overview

Background Globally, human rights violations experienced by persons with psychosocial, intellectual or cognitive disabilities continue to be a concern. The World Health Organization's (WHO) QualityRights initiative presents practical remedies to address these abuses. This paper presents an overview of the implementation of the initiative in Ghana.Aims The main objective of the QualityRights initiative in Ghana was to train and change attitudes among a wide range of stakeholders to promote recovery and respect for human rights for people with psychosocial, intellectual and cognitive disabilities.Method Reports of in-person and online training, minutes of meetings and correspondence among stakeholders of the QualityRights initiative in Ghana, including activities of international collaborators, were analysed to shed light on the implementation of the project in Ghana.Results In-person and online e-training on mental health were conducted. At the time of writing, 40 443 people had registered for the training, 25 416 had started the training and 20 865 people had completed the training and obtained a certificate. The team conducted 27 in-person training sessions with 910 people. The successful implementation of the project is underpinned by a committed partnership among stakeholders, strong leadership from the coordinating agency, the acceptance of the initiative and the outcome. A few challenges, both in implementation and acceptance, are discussed.Conclusions The exposure of the WHO QualityRights initiative to a substantial number of key stakeholders involved in mental healthcare in Ghana is critical to reducing human rights abuses for people with psychosocial, intellectual and cognitive disabilities

Case Report: Molecular characterization of rabies virus transmitted from a dog to a bull in a livestock market in Ghana

On the 24/09/2023, a video of a suspected rabid bull at a livestock market in Ghana was shared on social media and was seen by a local veterinary officer. This led to an on-site investigation by veterinary authorities on the 25/09/2023 which concluded that the bull had been bitten by a three-month old dog 4 days previously. The puppy, which was killed and buried after the bite, was subsequently exhumed, tested and confirmed positive for rabies. The bull was humanely destroyed. Brain tissue from the bull was collected and sent to the Accra Veterinary Laboratory for further analysis. RABV was confirmed by conventional RT-PCR and the full genome of the viruses from both animals were sequenced. The consensus sequences of the genomes belonging to the Africa 2 clade, were identical although sub-consensus variants in a subset of the sequences located in the RNA-dependent-RNA polymerase (L) gene of the bovine virus were observed. The implication of these findings is discussed

Cost-efficiency and bank profitability during health crisis

International audienc

Covid-19 pandemic, a catalyst for aggressive earnings management by banks?

International audienc

Effect of Compensation Package on Staff Intention to Quit in Technical University: A Structural Equation Approach

The paper analyses the compensation-intention to quit relationship in one of the technical universities in Ghana. Employing a descriptive survey design, data on the variables are collected using one hundred self-administered valid questionnaires. Data collected were analysed by means of structural equation modelling. The results show there is no significant relationship between intentions to quit and salary, incentives, allowance and fringe benefits. However, there was an inverse relationship between the dependent variable and the predictors. The result implies managers of such institutions do not focus only on monetary, but non-monetary rewards packages drawing their compensation plan. This study provides avenues for reviewing compensation packages of technical universities in order to motivate its employees to help prevent high labour turnover. The paper is among the few that employs the structural equation modelling in its analysis.</jats:p