Report on the first detection of Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in the Republic of Benin, West Africa

Sétamou, Mamoudou; Soto, Yovanna L.; Tachin, Martine Zandjanakou; Alabi, Olufemi J.

doi:10.1038/s41598-023-28030-3

Cited by 15 publications

(5 citation statements)

References 44 publications

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“…The role of rainfall on the increase and decrease of ACP population was also significant. Sétamou et al (2023) reported that heavy rainfall is linked with low population of psyllid, as this amount of rainfall washes out eggs and nymphs of psyllid, while low rainfall favours psyllid population (Devi and Sharma, 2014). The seasonal distribution of psyllid completely depends on the rainfall (Devi and Sharma, 2014).…”

Section: Effect Of Environmental Variables On Acp Population During Y...mentioning

confidence: 99%

Population Dynamics and Epidemiology of Diaphorina citri in Relation to Yield Losses Caused by Citrus Greening

Ahmad¹,

Muhammad²,

Majeed³

et al. 2023

PUJZ

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| Citrus greening (CG) or huanglongbing (HLB) is the major cause of citrus decline. The disease is currently prevailing in all districts of Punjab and posing severe threat to citrus production and export. The current research was designed with two objectives; (1) to study the population dynamics of Diaphorina citri, the vector of CG, and (2) to record the incidence of CG in different citrus growing areas of Punjab and estimation of losses caused by this disease.To study the dynamics of Asian citrus psyllid (ACP), a comprehensive survey of kinnow (Citrus reticulate cv. kinnow), musambi (Citrus sinensis cv. musambi) and feutrell's early (Citrus reticulate cv. feutrell's early) orchards was conducted in tehsil Sargodha during 2017 and 2018. Correlation and regression analysis were used to determine the relationship of ACP with CG, and the relationship of environmental factors with ACP. Surveys were conducted to record the incidence of CG and estimate the yield losses caused by it. The study of population dynamics of ACP revealed that ACP population was higher between 7 th to 14 th April and 11 th to 18 th September, during 2017 and 2018. While, there was less ACP population between February 10 th to 2 nd March and 10 th July to 30 th July, during 2017 and 2018, respectively. There was significant and positive correlation between ACP population and CG on kinnow, musambi and feutrell's early cultivars. Maximum and minimum temperatures and rainfall also showed positive and significant correlation with CG, while relative humidity showed not significant correlation with CG. During current study, maximum temperature, minimum temperature and rainfall explained maximum, i.e., 79, 76 and 83% variability in ACP population, respectively. The incidence of CG was observed higher (45%) in Faisalabad while lower (21.67%) in Multan. Similarly, yield losses due to CG were recorded maximum (33.5%) in Faisalabad and lowest (15.5%) in Multan districts. The current findings will help in the effective control of CG in Punjab by the in time management of its vector. NoveltyStatement | The epidemiology of D. citri and yield losses caused by CG have been studied first time in citrus belt of Punjab, Pakistan, during this research.

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Section: Effect Of Environmental Variables On Acp Population During Y...mentioning

confidence: 99%

Population Dynamics and Epidemiology of Diaphorina citri in Relation to Yield Losses Caused by Citrus Greening

Ahmad¹,

Muhammad²,

Majeed³

et al. 2023

PUJZ

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“…Digital image analyses for plant disease diagnosis are increasingly being used. Convolutional neural networks (CNNs) incorporating image analyses of individual plant leaves have been examined as classification models, particularly under a controlled laboratory environment [ 4 ]. For instance, deep learning techniques with machine learning are used in citrus disease diagnosis systems [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models

Dong,

Shiraiwa,

Pawasut

et al. 2024

Plants

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The vector-transmitted Citrus Greening (CG) disease, also called Huanglongbing, is one of the most destructive diseases of citrus. Since no measures for directly controlling this disease are available at present, current disease management integrates several measures, such as vector control, the use of disease-free trees, the removal of diseased trees, etc. The most essential issue in integrated management is how CG-infected trees can be detected efficiently. For CG detection, digital image analyses using deep learning algorithms have attracted much interest from both researchers and growers. Models using transfer learning with the Faster R-CNN architecture were constructed and compared with two pre-trained Convolutional Neural Network (CNN) models, VGGNet and ResNet. Their efficiency was examined by integrating their feature extraction capabilities into the Convolution Block Attention Module (CBAM) to create VGGNet+CBAM and ResNet+CBAM variants. ResNet models performed best. Moreover, the integration of CBAM notably improved CG disease detection precision and the overall performance of the models. Efficient models with transfer learning using Faster R-CNN were loaded on web applications to facilitate access for real-time diagnosis by farmers via the deployment of in-field images. The practical ability of the applications to detect CG disease is discussed.

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“…C ITRUS production and quality face significant threats from diseases, such as citrus canker, insect pests, water availability, and climate change (Syed-Ab-Rahman et al, 2022; Shafqat et al, 2021). Among these issues, citrus diseases have emerged as a primary factor contributing to reduced yield, compromised fruit quality, and shortened tree lifespan worldwide (Sétamou et al, 2023). As a result, citrus exports to key global markets, including the European Union (EU) and American citrus markets, are hindered due to quarantine measures aimed at controlling fruit diseases like citrus canker .…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Temporal Changes for Citrus Canker Growth Rate Using Deep Learning

Zainab,

Afzal,

Al-Shehari

et al. 2023

IEEE Access

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Citrus canker is among the major plant diseases caused by Xanthomonas citri which affects the quality and quantity of citrus fruit. This results in reduction of citrus production which causes a huge financial loss and livelihood of the farming community. Thus, it is critically important to build a robust, accurate, and time efficient detection method for real time identification of the disease. Due to their powerful learning capabilities and improved feature extraction, deep learning approaches have made it feasible to carry out a number of tasks related to the identification of citrus canker in citrus leaves. Previous research has primarily focused on detecting citrus canker on fruits, early detection on leaves can facilitate the adoption of preventive measures before the disease reaches a critical stage. This paper proposes a novel deep learning-based approach for determining the growth rate of citrus canker by classifying it into six distinct stages: water soaking, yellow chlorosis/initiation, chlorosis, blister formation, canker development start, canker infection (50% of the inoculated area), and canker infection (100% of the inoculated area). The proposed approach involves image conversion, size reduction, image augmentation, and the utilization of DenseNet-121. Experimental results demonstrate a classification accuracy of 98.97% using the suggested approach. The Accuracy was 98.97% with macro precession 97%, weighted precision 99%, Macro recall 98%, weighted recall 98%, macro F1_Score 97% and weighted F1_Score 98%.This study presents a unique technique for detecting and classifying the growth rate of citrus canker based on six different stages, while also calculating the temporal change in affected area of the disease in inoculated citrus leaves. Furthermore, a mathematical model is proposed to predict the disease's growth rate at any given time, offering valuable insights for disease management and prevention.

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Report on the first detection of Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in the Republic of Benin, West Africa

Cited by 15 publications

References 44 publications

Population Dynamics and Epidemiology of Diaphorina citri in Relation to Yield Losses Caused by Citrus Greening

Population Dynamics and Epidemiology of Diaphorina citri in Relation to Yield Losses Caused by Citrus Greening

Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models

Detection and Classification of Temporal Changes for Citrus Canker Growth Rate Using Deep Learning

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