Hermine BILLE NGALLE

Assessment of Genetic Variability and Character Association of Watermelon (<i>Citrullus lanatus</i>) varieties Using Agromorphological Characters

Fokam Paul Ernest, LIKENG-LI-NGUE BENOIT-CONSTANT, Ndiang Zenabou, Ntsomboh Ntsefong Godswill, Mafouasson Apala Hortense Noëlle, Molo Nathalie et 2 autre(s)

International Journal of Genetics and Genomics

Understanding genetic diversity and germplasm classification through agro-morphological characterization is fundamental in order to provide information for genetic resources conservation and breeding programs. The overall objective of this study was to assess the genetic variability of watermelon varieties cultivated in Cameroon. Five watermelon (<i>Citrullus lanatus</i>) genotypes were evaluated for genetic variability, heritability, and genetic advance for yield and its contributing characters in watermelon during 2017. Analysis of variance showed significant variation for all the studied characters, indicating the presence of sufficient variability in the material. Germination rates of the five varieties vary from 61.11 ± 5.82 to 90.71 ± 1.12% for Sugar Baby and Kaolack. Emergence time ranges from 7.03 ± 0.87 (Kaolack) to 8.73 ± 1.03 (Koloss), while flowering time varies from 28.27 ± 0.45 (Crimson Sweet) to 36.24 ± 0.32 (Sugar Baby) days. The number of fruits (fruit count) show that Kaolack has the highest number of fruits per plant (3.55 ± 0.89), followed by Crimson Sweet (3.32 ± 0.5) while Koloss presents the lowest number of fruits per plant (2.12 ± 0.27). Of the 45 correlations performed, 26 were negative and 19 were positive. All negative correlations were not significant at the 5% level, while 5 positive correlations were significant. This result shows that length of fruit is correlated with weight of fruit (0.99) and weight of one hundred seeds (0.88). Moreover, the correlation between these two fruit characteristics (weight of fruit and length of fruit) appeared strong and positive (0.92). The phenotypic coefficient of variation ranged from 20.35 to 201.49 for the seed shape and weight of fruit parameters, respectively, while the genotypic coefficient of variation ranged from 17.72 to 198.65 for the same traits, respectively. All the traits studied showed that the phenotypic variance was higher than the genotypic variance. High broad-sense heritability coupled with moderate-high genetic advance was recorded for weight of one hundred seeds (11.63), while high broad-sense heritability associated with low genetic advance values was obtained for seed shape (0.21), seedling emergence time (5.89), and number of fruits per plant (6.43). Only the first two axes of the principal component analysis were taken into account because they explained about 92.31% of the variation observed in the varieties. The main parameters associated with these two axes were weight of fruit, length of fruit, flowering time, weight of one hundred seeds and life cycle. Based on the variation, 5 genotypes were grouped into three classes using the K-means classification. Further studies involving biochemical and molecular markers are recommended for deeper characterization.

• #advances in cucurbitaceae research
• #seed and plant biochemistry
• #plant disease management techniques
• #genetic variability

Graph Cuts Segmentation Enhances Machine Assisted Plant Disease Diagnosis under Tight Data Constraints

Enow T. A. Albert, Ngalle Hermine Bille, Ngonkeu Mangaptche Eddy Léonard

International Journal of Complexity in Applied Science and Technology

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

• #smart agriculture and ai
• #remote sensing in agriculture
• #plant-microbe interactions and immunity
• #segmentation

Leveraging deep learning for plant disease identification: a bibliometric analysis in SCOPUS from 2018 to 2024

Enow Takang Achuo Albert, Ngalle Hermine Bille, Ngonkeu Mangaptche Eddy Léonard

Journal of Scientific Agriculture

This work aimed to present a bibliometric analysis of deep learning research for plant disease identification, with a special focus on generative modeling. A thorough analysis of SCOPUS-sourced bibliometric data from 253 documents was performed. Key performance metrics such as accuracy, precision, recall, and F1-score were analyzed for generative modeling. The findings highlighted significant contributions from some authors Too and Arnal Barbedo, whose works had notable citation counts, suggesting their influence on the academic community. Co-authorship networks revealed strong collaborative clusters, while keyword analysis identified emerging research gaps. This study highlights the role of collaboration and citation metrics in shaping research directions and enhancing the impact of scholarly work in applications of deep learning to plant disease identification. Future research should explore the methodologies of highly cited studies to inform best practices and policy-making.

• #plant pathogens and fungal diseases
• #plant pathogenic bacteria studies
• #phytoplasmas and hemiptera pathogens
• #scopus

Statistical optimization for plant disease classification using quantum adiabatic machine learning

Enow Takang Achuo Albert, Ngalle Hermine Bille, Ngonkeu Mangaptche Eddy Léonard

Journal of Applied and Advanced Research

Quantum Adiabatic Machine Learning (QAML) leverages principles of adiabatic quantum computation to optimize machine learning tasks such as plant disease classification. By encoding the problem into quantum Hamiltonians and using adiabatic evolution, QAML explores a vast solution space efficiently. This method uses quantum concepts such as entanglement to navigate complex energy landscapes that classical approaches find challenging. The framework applies both theoretical and practical insights to address high-dimensional data issues, essential for identifying plant diseases accurately and efficiently. We show that QAML optimizes classification tasks by encoding plant disease detection into an Ising-model-inspired Hamiltonian. The adiabatic process retains optimal configurations via multi-qubit entanglement while mitigating decoherence effects. Furthermore, we mathematically demonstrate QAML’s resilience to noise in open quantum systems and its potential quantum advantage over classical methods. This approach promises advancements in agricultural diagnostics and quantum-enhanced learning.

• #spectroscopy and chemometric analyses
• #adiabatic process
• #quantum machine learning
• #statistical learning

A mathematical primer to classical deep learning

Enow Takang Achuo Albert, Ngalle Hermine Bille, Ngonkeu Mangaptche Eddy Léonard

Journal of Applied and Advanced Research

This manuscript synthesizes the statistical foundations of classical deep learning by integrating insights from eight seminal works. It covers matrix calculus, neuron layers, weight and bias indexing, cost functions, differentiation of neuron operations, activation functions, bias functions, gradient descent, and backpropagation algorithms. The synthesis aims to provide a comprehensive understanding of the mathematical and statistical principles underpinning deep learning models, facilitating their application and further development in various domains.

• #history and theory of mathematics
• #primer (cosmetics)
• #artificial intelligence
• #computer science

Agro-morphological variability and prospects of genetic improvement of Hibiscus sabdariffa L. collections in some central Africa countries

LIKENG-LI-NGUE BENOIT-CONSTANT, Ndzana Ndzana Emile Lionel Landry, Aladji Abatchoua Madi Madi Ibram, Mbo Nkoulou Luther Fort, Molo Thierry, Molo Nathalie Shérifa et 3 autre(s)

Research Square

<title>Abstract</title> Roselle (<italic>Hibiscus sabdariffa</italic>) is a Malvaceae with multiple economic and pharmacological potential, little or poorly known and under-exploited in Cameroon. The aim of this study was to assess the genetic variability of 36 collections from several countries in the Central African sub-region, in an alpha lattice design (6x6) with two replications at the University of Yaoundé I. The data collected from five plants in each plot per replication were randomly selected from the middle row, excluding the border plants, and harvested at the stage of 50% flowering. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were estimated using the R software <italic>variability</italic> package (version 3.5.1). The analysis of variance results from the 20 traits evaluated including 13 qualitative and 7 quantitative showed significant differences (P &lt; 0.0001) between accessions at 60 days after sowing. Average heights to first branch ranged from 2.62 to 14.70 cm and the plant heights ranged from 10.5 to 86 cm. Mean collar diameter values varied between 0.40 and 1.36 cm. Collar diameter was strongly and positively correlated with the number of branches (r = 0.8) and with the 50% flowering date. Flower bud initiation date (DIBF) was negatively correlated with chlorophyll index (-0.6) and plant height (-0.4). Hierarchical clustering grouped the collections into 03 groups of 10 collections, 12 collections and 14 collections respectively. However, no differences were observed between collection origins for all parameters studied. High heritability was recorded for growth parameters such as collar diameter (0.6143), number of branches (0.6363), plant height (0.7240) and height of first branch (0.7653), revealing strong potential for selection and genetic improvement. The first two axes of the PCA which explained 64.7% were taken into account. The association of quantitative variables and collections showed the top 10 collections which represented potential parents for breeding programs.

• #hibiscus plant research studies
• #african botany and ecology studies
• #food science and nutritional studies
• #hibiscus sabdariffa

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Enow Takang Achuo Albert, Ngalle Hermine Bille, Ngonkeu Mangaptche Eddy Léonard

Journal of Scientific Agriculture

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

• #smart agriculture and ai
• #spectroscopy and chemometric analyses
• #cell image analysis techniques
• #principal component analysis

Assessment of Phytogenetic Resources of Chili Pepper (Capsicum annun L.) in Yaounde-Cameroon and their Supply Areas

Likeng Li Ngue Benoît-Constant, Ngalle Hermine Bille, Molo Thiery, Madeunou Nkouekeu Olga Laure, Godswill Ntsomboh Ntsefong, Zenabou Ndiang et 2 autre(s)

EAS Journal of Biotechnology and Genetics

A survey was performed in order to characterize the available phytogenetic resources of Chili pepper (Capsicum annun. L.). A sample of one hundred and twenty-six (126) individuals was used in the study. They included 85 traders, 25 producers and 16 seed promotion institutions from Yaounde, (Cameroon) and its supply areas. Respondents described eighteen varieties of pepper by production zones (12.91%) and colours (19.35%), while 65.59% of the respondents have no idea of the varieties marketed. Most of the pepper sold in the market, generally comes from areas such as Foumbot (25.71%), Bangante (13.33%), Okola (8.57%), Mbouda (6.97%), Mfou (5.71%) and unknown origin (21.9 %). The pepper is taken out of their production areas without any varietal identity, due to negligence on the part of producers, who buy 30% of the seedlings from the market, or extract their own seeds (42.5%) without prior identification of the variety. This leads to the loss of the genetic resource, despite the presence of several marketing structures with several well-known varieties. The lack of proper characterization leads to erosion of the pepper plant genetic resources in Yaounde and its supply areas. Added to this is the lack of interest of sellers, the high price of seeds, and the extraction of seeds from fruits after a production season or the use of seeds from the market.

• #agricultural pest management studies
• #agricultural practices and plant genetics
• #genetic and environmental crop studies
• #pepper

Genome Mapping to Enhance Efficient Marker-Assisted Selection and Breeding of the Oil Palm (&amp;lt;i&amp;gt;Elaeis guineensis&amp;lt;/i&amp;gt; Jacq.)

Essubalew Getachew Seyum, Ngalle Hermine Bille, Wosene Gebreselassie Abtew, Godswill Ntsomboh Ntsefong, Joseph M. Bell

Advances in Bioscience and Biotechnology

The oil palm (Elaeis guineensis Jacq.) is one of the major cultivated crops among the economically important palm species. It is cultivated mainly for its edible oil. For a perennial crop like oil palm, the use of Marker Assisted Selection (MAS) techniques helps to reduce the breeding cycle and improve the economic products. Genetic and physical maps are important for sequencing experiments since they show the exact positions of genes and other distinctive features in the chromosomal DNA. This review focuses on the role of genome mapping in oil palm breeding. It assesses the role of genome mapping in oil palm breeding and discusses the major factors affecting such mapping. Generating a high-density map governed by several factors, for instance, marker type, marker density, number of mapped population, and software used are the major issues treated. The general conclusion is that genome mapping is pivotal in the construction of a genetic linkage map. It helps to detect QTL and identify genes that control quantitative traits in oil palm. In perspective, the use of high-density molecular markers with a large number of markers, a large number mapping population, and up-to-date software is necessary for oil palm genome mapping.

• #oil palm production and sustainability
• #genetic diversity and population structure
• #genomics and phylogenetic studies
• #elaeis guineensis

Evidence of Minor Genes around the Major Gene Controlling Acidity of Oil palm (Elaeis guineensis Jacq.) Progenies from La Dibamba Germplasm

Likeng-Li-Ngue Benoit Constant, Ngando-Ebongue Georges Frank, Ngalle Hermine Bille, Godswill Ntsomboh Ntsefong, Nsimi Mva Armand, Bell Joseph Martin

Scholars Academic Journal of Biosciences

In Cameroon, most of the crude palm oil (CPO) consumed locally is produced by the informal sector and is generally of poor quality. The quality of palm oil is mainly determined by its acidity which is generated by a very active lipase present in the mesocarp of ripe palm fruits. Previous studies showed a strong variability of this trait amongst progenies, and the segregation of forms suggested a monohybridism with dominance of the strong acidity allele. The objective of this study was to get more insights into this mechanism by explaining the phenotypic variability of palm oil acidity (POA) in view of possible production of seeds bearing this low oil acidity trait. Individual palms were grouped in classes based on their level of acidity from the mean and standard deviation. Figures of each class of acidity were compared to the theoretical proportions derived from the normal distribution using Chi square test ( 2 ) at 5% significance threshold according to the genetic determinism of POA previously demonstrated. POA assessment of 650 palms indicated a strong variability, with values ranging from 0.5 to 42% and an average of 10 8%. The normal distribution of F 2 into 3 classes around 1.34 0.6, 15 6 and 21.92 5%, corresponded to the genotypes papa , Papa and PaPa respectively conferring low, average and high acidity traits. This suggest the presence of minor genes around the major gene controlling POA, with the dominance of high acidity form.

• #oil palm production and sustainability
• #coconut research and applications
• #plant biochemistry and biosynthesis
• #elaeis guineensis

Genotyping by Sequencing for Plant Breeding- A Review

Seyum Essubalew Getachew, Ngalle Hermine Bille, Joseph Martin Bell, Wosene Gebreselassie

• #genetics, bioinformatics, and biomedical research
• #plant genetic and mutation studies
• #chromosomal and genetic variations
• #genotyping

Variation in Seeds Physical Traits of Bambara Groundnut (Vigna subterranea) Collected in Cameroon

Zenabou Ndiang, Bell Joseph Martin, Bassiaka Ouattara, Mokake Ebenye, Ngalle Hermine Bille, Daniel Foncéka

Journal of Experimental Agriculture International

International audience

• #agricultural pest management studies
• #agricultural engineering and mechanization
• #genetics and plant breeding
• #vigna