Black Sigatoka in bananas: Ecoclimatic suitability and disease pressure assessments

Classes and phyla of the kingdom Fungi

Fungi are one of the most diverse groups of organisms with an estimated number of species in the range of 2–3 million. The higher-level ranking of fungi has been discussed in the framework of molecular phylogenetics since Hibbett et al., and the definition and the higher ranks (e.g., phyla) of the ‘true fungi’ have been revised in several subsequent publications. Rapid accumulation of novel genomic data and the advancements in phylogenetics now facilitate a robust and precise foundation for the higher-level classification within the kingdom. This study provides an updated classification of the kingdom Fungi, drawing upon a comprehensive phylogenomic analysis of Holomycota, with which we outline well-supported nodes of the fungal tree and explore more contentious groupings. We accept 19 phyla of Fungi, viz. Aphelidiomycota, Ascomycota, Basidiobolomycota, Basidiomycota, Blastocladiomycota, Calcarisporiellomycota, Chytridiomycota, Entomophthoromycota, Entorrhizomycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota, Sanchytriomycota, and Zoopagomycota. In the phylogenies, Caulochytriomycota resides in Chytridiomycota; thus, the former is regarded as a synonym of the latter, while Caulochytriomycetes is viewed as a class in Chytridiomycota. We provide a description of each phylum followed by its classes. A new subphylum, Sanchytriomycotina Karpov is introduced as the only subphylum in Sanchytriomycota. The subclass Pneumocystomycetidae Kirk et al. in Pneumocystomycetes, Ascomycota is invalid and thus validated. Placements of fossil fungi in phyla and classes are also discussed, providing examples.

Analysis of banana plant health using machine learning techniques

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Impacts of climate change and host plant availability on the potential distribution of Bradysia odoriphaga (Diptera: Sciaridae) in China

BACKGROUND

Chinese chives (Allium tuberosum Rottler ex Sprengel) are favored by consumers because of its delicious taste and unique fragrance. Bradysia odoriphaga (Diptera: Sciaridae) is a main pest that severely harms Chinese chives and other Liliaceae's production. Climate change may change the future distribution of B. odoriphaga in China. In this study, the CLIMEX was employed to project the potential distribution of B. odoriphaga in China, based on China's historical climate data (1987-2016) and forecast climate data (2021-2100).

RESULTS

Bradysia odoriphaga distributed mainly between 19.8° N-48.3° N and 74.8° E-134.3° E, accounting for 73.25% of the total mainland area of China under historical climate conditions. Among them, the favorable and highly favorable habitats accounted for 30.64% of the total potential distribution. Under future climate conditions, B. odoriphaga will be distributed mainly between 19.8° N-49.3° N and 73.8° E-134.3° E, accounting for 84.89% of China's total mainland area. Among them, the favorable and highly favorable habitats will account for 35.23% of the total potential distribution, indicating an increase in the degree of fitness. Areas with relatively appropriate temperature and humidity will be more suitable for the survival of B. odoriphaga. Temperature was a more important determinant of the climatic suitability of the pest B. odoriphaga than humidity. Host plants (Liliaceae) availability also had impact on climate suitability in some regions.

CONCLUSIONS

These projected potential distributions will provide supportive information for monitoring and early forecasting of pest outbreaks, and to reduce future economic and ecological losses. © 2024 Society of Chemical Industry.

Banana Plant Disease Classification Using Hybrid Convolutional Neural Network

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Potential global distribution of Aleurocanthus woglumi considering climate change and irrigation

Citrus blackfly, Aleurocanthus woglumi Ashby (Hemiptera: Aleyrodidae), is an important agricultural quarantine pest, causing substantial economic losses to citrus and many other cultivated crops. Aleurocanthus woglumi is found in tropical and subtropical regions but is presently unknown in Europe and the Mediterranean Basin. We used CLIMEX to model the potential distribution of A. woglumi under an historical climate scenario (centred on 1995), including a spatially explicit irrigation scenario. We found that A. woglumi could potentially invade the Mediterranean Basin, and south-east Asia, including Australia. There is potential for it to invade most of sub-Saharan Africa. Irrigation is revealed as an important habitat factor affecting the potential distribution of A. woglumi, increasing its potential range by 53% in Asia. Under a future climate scenario for 2050, its potential distribution increased across all continents except Africa, where potential range expansion due to relaxation of cold stresses was limited, and was offset by range decrease due to lethal heat or dry stress. As global climates warm, Europe is likely to face a substantial increase in the area at risk of establishment by A. woglumi (almost doubling under the 2050 irrigation scenario). The biosecurity threat from A. woglumi is significant in current citrus production areas and poses a challenge to biosecurity managers and risk analysts.

Distribution and invasion risk assessments of Chrysodeixis includens (Walker, [1858]) (Lepidoptera: Noctuidae) using CLIMEX

Chrysodeixis includens is a polyphagous pest restricted to the American continent. The occurrence of C. includens is allied, among other factors, by favorable conditions such as temperature, humidity, presence of hosts, and migratory behavior. In this work, we built spatiotemporal species distribution models at continental and global levels for the distribution of C. includens using CLIMEX to determine times and regions favorable for year-round survival and migration of this species and in case of invasion on other continents to apply timely and right phytosanitary measures. Our models estimated high climate suitability for C. includens in Central and large proportions of South America throughout the year. Moreover, there is suitability for C. includens growth in all months of the year in Central and northern part of South America. In the northern hemisphere, these conditions range from April to October, while in mid-southern parts of South America, favorable periods comprise October through June. The countries with the highest suitability for C. includens outside the American continent are located on the African and Asian continents. Our results show variable climate suitability for C. includens during the year that help to understand likely migration pattern in North America. This information would direct efforts for appropriate C. includens management during warm and moist periods of the year. Furthermore, our models notify the need for the development of strategies for the inspection and interception of C. includens especially in central Africa, India, South and Southeast Asia, and Northeast Australia.

Modelling the Potential Geographic Distribution of Two Trissolcus Species for the Brown Marmorated Stink Bug, Halyomorpha halys

Simple Summary

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), native to Asia, has been accidentally introduced to Europe and North America, where it has become a key pest by feeding on numerous important crops. Although H. halys has not yet established in Australia, there is a general consensus that this is only a matter of time, and thus, it is prudent to investigate management options. Previous studies have modelled the potential distribution of H. halys and one of its principal natural enemies, Trissolcus japonicus (Ashmead) (Hymenoptera: Scelionidae). Here, we developed a similar model of the potential distribution of Trissolcus mitsukurii (Ashmead), which is a primary parasitoid of H. halys in Japan, and which was introduced to Australia in the 1960s to control another introduced pest. We used the three models to examine the overlap in the projected distributions of both T. mitsukurii and T. japonicus with H. halys, and to assess the potential for the two Trissolcus species to help mitigate the impacts of H. halys in its global adventive range.

Abstract

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), is native to northeast Asia. It was accidentally introduced to Europe and North America, where it has become a key pest, feeding on many important crops. Previous eco-climatic niche modelling indicates that H. halys could expand its distribution vastly, and numerous border interceptions of this pest in many countries, including Australia and New Zealand, indicate that it would be prudent to prepare for its eventual arrival. Similar niche modelling was used to assess the potential distribution of Trissolcus japonicus (Ashmead) (Hymenoptera: Scelionidae), the key parasitoid of H. halys in China. Trissolcus mitsukurii (Ashmead) is one of the main parasitoids of H. halys in Japan. It is known to have existed in Australia since the early 20th century and was also specifically introduced to Australia in the 1960s, and it has now also invaded Italy. We used CLIMEX to model the climatic niche of T. mitsukurii to estimate its global potential distribution. We found that T. mitsukurii should be able to significantly expand its range globally, and that there is a significant degree of overlap in the projected ranges of T. mitsukurii, T. japonicus and H. halys. From a biological control perspective, this implies that the two Trissolcus species may be able to help mitigate the potential impacts of H. halys.

Genetic Improvement for Resistance to Black Sigatoka in Bananas: A Systematic Review

Bananas are an important staple food crop in tropical and subtropical regions in Asia, sub-Saharan Africa, and Central and South America. The plant is affected by numerous diseases, with the fungal leaf disease black Sigatoka, caused by Mycosphaerella fijiensis Morelet [anamorph: Pseudocercospora fijiensis (Morelet) Deighton], considered one of the most economically important phytosanitary problem. Although the development of resistant cultivars is recognized as most effective method for long term control of the disease, the majority of today's cultivars are susceptible. In order to gain insights into this pathosystem, this first systematic literature review on the topic is presented. Utilizing six databases (PubMed Central, Web of Science, Google Academic, Springer, CAPES and Scopus Journals) searches were performed using pre-established inclusion and exclusion criteria. From a total of 3,070 published studies examined, 24 were relevant with regard to the Musa-P. fijiensis pathosystem. Relevant papers highlighted that resistant and susceptible cultivars clearly respond differently to infection by this pathogen. M. acuminata wild diploids such as Calcutta 4 and other diploid cultivars can harbor sources of resistance genes, serving as parentals for the generation of improved diploids and subsequent gene introgression in new cultivars. From the sequenced reference genome of Musa acuminata, although the function of many genes in the genome still require validation, on the basis of transcriptome, proteome and biochemical data, numerous candidate genes and molecules have been identified for further evaluation through genetic transformation and gene editing approaches. Genes identified in the resistance response have included those associated with jasmonic acid and ethylene signaling, transcription factors, phenylpropanoid pathways, antioxidants and pathogenesis-related proteins. Papers in this study also revealed gene-derived markers in Musa applicable for downstream application in marker assisted selection. The information gathered in this review furthers understanding of the immune response in Musa to the pathogen P. fijiensis and is relevant for genetic improvement programs for bananas and plantains for control of black Sigatoka.