Biology subjects

Boonham, N.

Publications and source records attributed to Boonham, N..

8 recordsLinked to original sources

bioRxiv2025

Synergistic 3D, multispectral, and thermal image analysis via supervised machine learning for improved detection of root rot symptoms in hydroponically-grown flat-leaf parsley

Root rot in hydroponically-grown leafy vegetables is difficult to detect via conventional manual and machine vision-based approaches as symptoms of infection are not clearly visible on the canopy at earlier stages of infection. Hence, the present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection. Herein, flat-leaf parsley seedlings were grown in an experimental hydroponic vertical farm and inoculated with Pythium irregulare and Phytophthora nicotianae. Subsequently, the seedlings were imaged via 3D, multispectral, and thermal sensors at various stages of growth to obtain twenty-six image-based plant features. Following a preliminary screening of redundant features via regression analysis, data for seventeen image features associated with morphometric, spectral, and thermal attributes was co-analyzed using supervised machine learning by Support Vector Machines (SVM). Exhaustive feature selection using different SVM kernels and maximum feature thresholds was performed to identify optimal feature subsets. It was observed that combining parameters obtained from all three imaging platforms enabled better identification of infected samples (>99%) than using a higher number of attributes from individual imaging systems. In addition, model performance was improved considerably by including temporal information during model training. Hence, it may be inferred that fusion of data from multiple imaging systems and using it with temporal information can enable better real-time high-throughput monitoring of root rot.

plant biology↗

bioRxiv2024

Two-fold Red Excess (TREx): A simple and novel digital color index that enables non-invasive real-time health assessment of green-leaved as well as anthocyanin-rich crops

BackgroundDigital color indices provide a reliable means for assessing plant health status by enabling real-time estimation of chlorophyll (Chl) content, and are thus adopted widely for crop monitoring. However, as all prevalent leaf color indices used for this purpose have been developed using green-leaved plants, they do not perform reliably for anthocyanin (Anth)-rich red-leaved varieties. Hence, the present study investigates digital color indices for six types of leafy vegetables with different levels of Anth to identify congruent trends that could be implemented universally for non-invasive crop monitoring irrespective of species and leaf Anth content. For this, datasets from three digital color spaces, viz., RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and L*a*b* (Lightness, Redness-greenness, Yellowness-blueness), as well as various derived plant color indices were compared with SPAD Chl meter readings and Anth/Chl ratio of n = 320 leaf samples. ResultsWhile most digital color features and indices presented abrupt shifts between Anth-rich and green-leaved samples, the newly-developed color index Two-fold Red Excess (TREx) as well as the color feature R showed very strong correlation with SPAD readings (R2 > 0.84), and did not exhibit any deviation due to leaf Anth content. Moreover, both parameters could predict SPAD values reliably (R2 > 0.75). Further, logarithmic decline of G/R and Augmented Green-Red Index (AGRI) with increasing Anth/Chl ratio (R2 > 0.82) revealed that relative Anth content affected digital color indices markedly by shifting the R:G balance until the Anth/Chl ratio reached a certain threshold. ConclusionThe present study provides the first in-depth assessment of variations in RGB-based digital color indices due to high leaf Anth contents, and uses the data for Anth-rich as well as green-leaved crops belonging to different species to develop a universal digital color index TREx that can be used as a reliable alternative to handheld Chl meters for rapid high-throughput monitoring of green-leaved as well as red-leaved crop varieties.

plant biology↗

bioRxiv2024

Identifying the Fusarium species involved in foot rot disease of faba beans in the UK using a combined molecular and microbiological approach

Foot rot is a devastating disease of faba bean crops globally, including in the United Kingdom, the worlds third largest producer. To identify the causal agents, we have sampled foot rot-affected plants and soils from faba bean crops across England. We isolated organisms associated with foot rot disease in culture and assessed pathogenicity in vivo to evaluate the infectivity of the isolates on faba bean. We identified the pathogenic isolates using DNA barcoding of the Internal Transcribed Spacer (ITS) and Translation Elongation Factor one (TEF1 ) molecular markers. A total of 113 clonal isolates were obtained from infected plants and soil samples across England. Of these, 60 were pathogenic, inducing mild to severe symptoms on faba bean. Sequencing of the ITS and TEF1 loci and comparison against sequence databases (Genbank and Fusarium_ID) enabled the identification of pathogenic isolates, in decreasing order of frequency, as Fusarium oxysporum (26.6 %), F. vanettenii (25%), F. redolens (15 %), F. solani (11.6%), F. culmorum (8.3 %), F. avenaceum (6.7 %), F. equiseti (1.7 %), F. clavum (1.7 %), Clonostachys rosea (1.7%) and Alternaria alternata (1.7%). F. oxysporum, F. redolens and F. avenaceum induced the most severe symptoms, whilst F. solani induced the least severe symptoms. Determining the most prevalent causal agents of foot rot in UK faba beans will facilitate targeted disease monitoring and intervention for enhanced productivity.

microbiology↗

bioRxiv2024

Infrared thermography for plant stress detection in vertical farms: Investigating spatiotemporal variations and exploring solutions via machine learning

Application of infrared thermography (IRT) for real-time plant stress detection has grown rapidly in recent years. Although the technology has been well established for crops grown in fields and glasshouses, its feasibility for vertical farms has not been tested extensively. In this study, temporal monitoring of stress induced by root dehydration in purple basil plantlets inside a vertical farm was performed to identify bottlenecks in real-time stress detection via IRT. Subsequently, potential solutions were investigated via machine learning by implementing support vector machines for supervised classification. Edge effects as well as proximity to air vents were identified as the major causes of positional variation in plant temperature that could lead to misprediction of stress. Binary, ternary, and quaternary classification models were trained using thermal images from two, three, and four levels of stress, respectively, to assess model performance. Binary classification models trained with plants experiencing medial and high levels of stress were able to identify stressed plants with high accuracy (81-94%). Further, binary models trained using plants under medial levels of stress generated a continuous probability distribution for stress prediction when plotted against plant temperature. In contrast, models trained using samples experiencing high stress generated distinct probabilistic clusters for the unstressed and highly stressed plants, but were unable to classify medial stress samples reliably. Similarly, ternary and quaternary models were able to better predict very high and very low levels of stress than intermediate stress levels. Hence, our findings suggest that binary classification models trained using samples under medial levels of stress would be helpful in overcoming spatiotemporal variations in canopy thermal profile by providing reliable probabilistic estimates of plant stress within a vertical farming system. Key pointsO_LIPlant stress detection in vertical farms via thermal imaging may be challenging because perceptible plant temperature can be strongly influenced by its microenvironment. C_LIO_LIThermal image analysis via supervised machine learning allows the development of robust prediction models that can overcome such factors to identify stressed plants. C_LIO_LIBinary classification machine learning models can reliably identify stressed plants as well as provide probabilistic estimates for the degree of stress. C_LI

plant biology↗

bioRxiv2024

Assessing nutritional pigment content and plant health status of green and red leafy vegetables by image analysis: Catching the 'red herring' of plant digital color processing

Estimating pigment content in leafy vegetables via digital image analysis is a reliable method for real-time assessment of plant health status and nutritive value. However, the present leaf color analysis models developed using green-leafed plants do not perform reliably while analyzing images of anthocyanin-rich leaves, often giving misleading or "red herring" trends. Hence, the present study investigates variations in different digital color features for six types of leafy vegetables with varying levels of laminar anthocyanin to identify holistic digital color analysis models that could be implemented for real-time assessment of health status as well as nutritional pigment contents of leafy vegetables irrespective of laminar anthocyanin status. For this, datasets from three digital color spaces, viz., RGB (red, green, blue), HSI (hue, saturation, intensity), and L*a*b* (lightness, redness-greenness, yellowness-blueness), were compared with pigment contents of n = 320 leaf samples, and were analyzed via linear and non-linear regression using single variables, multiple linear regression, Support Vector regression, and Random Forest regression to predict chlorophyll and anthocyanin contents. While most digital color features presented abrupt shifts between anthocyanin-rich and low-anthocyanin samples, the R digital color feature did not show any deviation due to leaf anthocyanin content and was found to have the best correlation with SPAD chlorophyll meter readings (R2 = 0.83). Concomitantly, H (R2 = 0.82) and a* (R2 = 0.79) features correlated most strongly with leaf anthocyanin content. In general, prediction of pigment contents was more accurate when data from all channels within a color space was analyzed simultaneously. Further, most reliable estimates of pigment content were provided by Support Vector and Random Forest regression models (0.7 < R2 < 0.85). Thus, the present findings demonstrate how digital color analysis of green as well as anthocyanin-rich leafy vegetables could be implemented for assessing plant health status and nutritional pigment content non-invasively.

plant biology↗

bioRxiv2018

Understanding biodiversity at the pondscape using environmental DNA: a focus on great crested newts

Environmental DNA (eDNA) metabarcoding is revolutionising biodiversity monitoring, but has unrealised potential for ecological hypothesis generation and testing. Here, we validate this potential in a large-scale analysis of vertebrate community data generated by eDNA metabarcoding of 532 UK ponds. We test biotic associations between the threatened great crested newt (Triturus cristatus) and other vertebrates as well as abiotic factors influencing T. cristatus detection at the pondscape. Furthermore, we test the status of T. cristatus as an umbrella species for pond conservation by assessing whether vertebrate species richness is greater in ponds with T. cristatus and higher T. cristatus Habitat Suitability Index (HSI) scores. T. cristatus detection was positively correlated with amphibian and waterfowl species richness. Specifically, T. cristatus was positively associated with smooth newt (Lissotriton vulgaris), common coot (Fulica atra), and common moorhen (Gallinula chloropus), but negatively associated with common toad (Bufo bufo). T. cristatus detection did not significantly decrease as fish species richness increased, but negative associations with common carp (Cyprinus carpio), three-spined stickleback (Gasterosteus aculeatus) and ninespine stickleback (Pungitius pungitius) were identified. T. cristatus detection was negatively correlated with mammal species richness, and T. cristatus was negatively associated with grey squirrel (Sciurus carolinensis). T. cristatus detection was negatively correlated with larger pond area, presence of inflow, and higher percentage of shading, but positively correlated with HSI score, supporting its application to T. cristatus survey. Vertebrate species richness was significantly higher in T. cristatus ponds and broadly increased as T. cristatus HSI scores increased. We reaffirm reported associations (e.g. T. cristatus preference for smaller ponds) but also provide novel insights, including a negative effect of pond inflow on T. cristatus. Our findings demonstrate the prospects of eDNA metabarcoding for ecological hypothesis generation and testing at landscape scale, and dramatic enhancement of freshwater conservation, management, monitoring and research.

ecology↗

bioRxiv2017

Needle in a haystack? A comparison of eDNA metabarcoding and targeted qPCR for detection of great crested newt (Triturus cristatus)

Environmental DNA (eDNA) analysis is a rapid, cost-effective, non-invasive biodiversity monitoring tool which utilises DNA left behind in the environment by organisms for species detection. The method is used as a species specific survey tool for rare or invasive species across a broad range of ecosystems. Recently, eDNA and metabarcoding have been combined to describe whole communities rather than focusing on single target species. However, whether metabarcoding is as sensitive as targeted approaches for rare species detection remains to be evaluated. The great crested newt Triturus cristatus is a flagship pond species of international conservation concern and the first UK species to be routinely monitored using eDNA. We evaluate whether eDNA metabarcoding has comparable sensitivity to targeted real-time quantitative PCR (qPCR) for T. cristatus detection. Extracted eDNA samples (N = 532) were screened for T. cristatus by qPCR and analysed for all vertebrate species using High-Throughput Sequencing technology. With qPCR and a detection threshold of 1/12 positive qPCR replicates, newts were detected in 50% of ponds. Detection decreased to 32% when the threshold was increased to 4/12 positive qPCR replicates. With metabarcoding, newts were detected in 34% of ponds without a detection threshold, and in 28% of ponds when a threshold (0.028%) was applied. Therefore, qPCR provided greater detection than metabarcoding but metabarcoding detection with no threshold was equivalent to qPCR with a stringent detection threshold. The proportion of T. cristatus sequences in each sample was positively associated with the number of positive qPCR replicates (qPCR score) suggesting eDNA metabarcoding may be indicative of eDNA concentration. eDNA metabarcoding holds enormous potential for holistic biodiversity assessment and routine freshwater monitoring. We advocate this community approach to freshwater monitoring to guide management and conservation, whereby entire communities can be initially surveyed to best inform use of funding and time for species-specific surveys.

molecular biology↗

bioRxiv2017

Characterising maize viruses associated with maize lethal necrosis symptoms in sub Saharan Africa

Maize lethal necrosis disease (MLN) is an emerging disease in East Africa caused by the introduction of Maize chlorotic mottle virus (MCMV). Recent activity seeking to limit spread of the disease is reliant on effective diagnostics. Traditional diagnostics applied on samples with typical field symptoms of MLN have often given negative results using ELISA or PCR for MCMV and Sugarcane mosaic virus (SCMV). Samples collected in the field with typical MLN symptoms were examined using next generation sequencing (NGS). SCMV was found to be more prevalent than suggested by targeted diagnostics. Additionally, the panel of samples were found to be infected with a range of other viruses, seven of which are described here for the first time. Although not previously identified in the region, Maize yellow mosaic virus (MYMV) was the most prevalent virus after MCMV. The development of targeted diagnostics for emerging viruses is complicated when the extent of field variation is unknown, something that can be negated by using NGS methods. As a result we explored MinION technology which may be more readily deployable in resource poor settings. The results show that this sequencer can diagnose known viruses and future iterations have the potential to identify novel viruses.

microbiology↗