Pepper Leaf Research Articles

Description of two new genera, three new species, and redescription of two species of Pronematinae (Acari: Iolinidae)

Duoparus n. gen. with its type species Duoparus hyeresensis n. gen. n. sp. from Rubus in France are described. Duoparus is the first genus within the Pronematinae with two ag setae. In the genus Pseudopronematulus, two new species, P. duffelensis n. sp. from sweet pepper leaf litter in Belgium and Pseudopronematulus tocantinsensis n. sp. from leaf/soil litter from a no-tillage field in Brazil are described. Pseudopronematulus tenuisetosus, recently transferred from the genus Pronematus, from cotton in Mozambique is redescribed. Pseudopronematulus duffelensis n. sp. and P. tocantinsensis n. sp. are the fourth and fifth species in this genus and the first to be described in detail, including nymphal stages for P. duffelensis n. sp. Dasilcoferla n. gen. is described with Pseudopronematulus nadirae n. comb. as type species, which is transferred from Pseudopronematulus to Dasilcoferla and redescribed. A taxonomic key is provided for the separation of the species presently placed in Pseudopronematulus Fan and Li, 1992. Molecular analysis and morphological differences, mostly the chaetotaxy, of P. duffelensis n. sp. are compared with those of Pronematus ubiquitus (McGregor, 1932) and Homeopronematus anconai (Baker, 1943). The molecular results underpin the morphological difference between the species that belong to different genera. Relative distance of the genera are discussed.

Effects of polyphenols from Tasmannia lanceolata on structural, emulsifying, and antioxidant properties of pea protein

The effects of polyphenols from Tasmanian pepper (Tasmannia Lanceolata) leaf and berry on the functional properties of pea protein were investigated in flaxseed oil-in-water emulsions. Phenolic acids and flavonols in Tasmanian pepper leaf with smaller molecular weights led to stronger non-covalent interactions with pea protein, while anthocyanins from Tasmanian pepper berry induced protein aggregation under acidic condition and co-existed with proteins in neutral and alkaline conditions. The total phenolic content was significantly increased with incorporation of polyphenols from Tasmanian pepper leaf (334.94–445.92 μg/mL) and berry (72.89–153.03 μg/mL) to pea protein (4.19–15.59 μg/mL). The oxidative stability of emulsions at pH 3 and 7 was enhanced, reducing TBARS value from 1.54 to 2.68 mg MDA/kg in pea protein to 0.56–0.85 mg MDA/kg after 2 weeks storage. These findings illustrated the distinct interactions between pea protein and different polyphenols from Tasmanian pepper leaf and berry to enhance the antioxidant capacity of pea protein.

First report of pepper leaf curl virus causing leaf curl of castor bean (Ricinus communis) in India

First report of pepper leaf curl virus causing leaf curl of castor bean (<i>Ricinus communis</i>) in India

Analysis of Bell Pepper (Capsicum annuum L.) Leaf Spectral Properties and Photosynthesis According to Growth Period

This study analyzed the leaf spectral properties and photosynthesis rates of greenhouse-grown bell pepper leaves according to the growth period and leaf position to investigate the changes in carbon assimilation function according to leaf aging. Photosynthesis, growth, transpiration, stomatal conductance, light transmittance, and light reflectance were measured. As the plants’ growth progressed, the number of leaves, fresh weight, and dry weight increased, but the specific leaf area decreased, likely due to the increased distribution of assimilates to reproductive organs. The average photosynthesis rate, according to the measured dates, exhibited a high value despite a large standard error, which was likely influenced by measurement errors caused by external environmental factors. The reflectance and transmittance increased from the upper to the middle and bottom leaves, and the absorption ratio decreased in the same order. The green light spectrum (500–580 nm) had a lower absorption ratio than other spectra because the green coloration of the leaves increased the light reflectance of this spectrum. As the PPFD increased where the leaf was positioned higher, the photosynthesis rate, transpiration amount, and stomatal conductance also increased. The higher the leaf position, the higher the photosynthesis rate, the amount of transpiration, and the stomatal conductance. As the CO2 concentration increased, the photosynthesis rate increased, but the transpiration and stomatal conductance changed little, indicating that the gas exchange within leaves was hardly affected by CO2, but the light levels promoted photosynthesis. From the results of this study, the optical properties of the leaves indicate that they are consistent with Lambert–Beer’s law, which implies that the length of the optical path is linearly proportional to the number of molecules in the absorption layer. We obtained the light saturation point and CO2 saturation point of bell peppers grown in a greenhouse and were able to determine the physiological changes in the leaves with increasing leaf age. Therefore, based on this information, it appears that a leaf removal model based on the productivity of bell pepper leaves could be developed.

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet.

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model's performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

A lightweight CNN model for pepper leaf disease recognition in a human palm background

The identification of pepper leaf diseases is crucial for ensuring the safety and quality of pepper yield. However, existing methods heavily rely on manual diagnosis, resulting in inefficiencies and inaccuracies. In this study, we propose a lightweight convolutional neural network (CNN) model for recognizing pepper leaf diseases and subsequently develop an application based on this model. To begin with, we acquired various images depicting healthy leaves as well as leaves affected by viral diseases, brown spots, and leaf mold. It is noteworthy that these images were captured against a background of human palms, which is commonly encountered in field conditions. The proposed CNN model adopts the GGM-VGG16 architecture, incorporating Ghost modules, global average pooling, and multi-scale convolution. Following training with the collected image dataset, the model was deployed on a mobile terminal, where an application for pepper leaf disease recognition was developed using Android Studio. Experimental results indicate that the proposed model achieved 100 % accuracy on images with a human palm background, while also demonstrating satisfactory performance on images with other backgrounds, achieving an accuracy of 87.38 %. Furthermore, the developed application has a compact size of only 12.84 MB and exhibits robust performance in recognizing pepper leaf diseases.

Analysis of the insecticidal effect of botanical extracts on mealybugs (Phenacoccus solenopsis)

The Cotton mealybug, also known as Phenacoccus solenopsis, poses a significant threat to agricultural productivity, particularly in cotton cultivation, where infestations cause substantial yield losses. This study examined the effectiveness of botanical extracts from neem (Azadirachta indica), clove (Syzygium aromaticum), black pepper (Piper nigrum), and bay leaf (Laurus nobilis) in controlling P. solenopsis. Neem extract showed the highest mortality effect at 86.6% followed by clove (68.3%) and black pepper (58.3%). Bay leaf extract had the lowest mortality effect at 36.6%. These findings highlight the potential of neem, clove, and black pepper extracts as effective options for managing P. solenopsis infestations in agriculture.

Analysis of biological organic extract of plants in suppressing aphid (Myzus persicae) infestations

Myzus persicae commonly known as the peach-potato aphid, poses a significant threat to agricultural productivity, particularly in potato cultivation, where infestations lead to substantial yield losses and quality degradation. In this study, we investigated the potential of botanical extracts from neem (Azadirachta indica), clove (Syzygium aromaticum), black pepper (Piper nigrum), and bay leaf (Laurus nobilis) in controlling Myzus persicae infestations. Experimental trials were conducted to assess the mortality effects of these botanical extracts on Myzus persicae populations. Results revealed that neem extract exhibited the highest mortality effect, with an impressive 97.7% mortality rate. Clove and black pepper extracts followed closely, demonstrating mortality rates of 92.2% and 77.7%, respectively. In contrast, bay leaf extract exhibited the least mortality effect, with only 48.8% mortality observed.

Botanical extracts improve postharvest quality and extend the shelf life of papaya (Carica papaya L. cv. Shahi)

ABSTRACT Though papaya is a lucrative fruit regarding nutritional, medicinal, and commercial attributes, its susceptibility to postharvest loss may account for ≥ 40%. An experiment was carried out at Khulna University, Bangladesh from April to May 2013 to investigate the effects of botanical extracts on the postharvest physicochemical attributes, intensity, and severity of diseases and shelf life of papaya cv. Shahi. The experiment comprised botanical extracts from onion bulb, neem leaf, garlic bulb, oriental pepper leaf, datura leaf, and control as six treatments and replicated thrice following a completely randomised design (CRD). Among the physicochemical parameters, total soluble solids (TSS), weight loss, and dry matter of papaya fruit increase, and pulp to peel ratio, moisture content decrease gradually during the storage period disregarding treatments. Garlic bulb extract decreases the incidence and severity of postharvest diseases and extends the shelf life by more than 7 days compared to control. Thus, garlic bulb extract can be used for postharvest papaya storage. However, further experiments should be conducted before the final recommendation.

Conformation and functional modification of porcine myofibrillar protein by pepper leaf polyphenols under oxidative condition

The effects of pepper leaf polyphenols (PLPs) on the conformation and functional characteristics of porcine myofibrillar proteins (MPs) under oxidative conditions were investigated. The addition of PLPs remarkably retarded the increase of carbonyl content and the average particle size of MPs after oxidation. Moreover, the sulfhydryl content, solubility, and the storage modulus (G′) of oxidized MPs were improved with the increased addition of PLPs to 1 g/100 mL. PLPs contributed to an increment in surface hydrophobicity when the incorporation of PLPs was 0.5 g/100 mL. The intrinsic fluorescence strength and FT-IR results indicated that PLPs altered protein conformation by exposure of the hydrophobic group and the conversion of α-helices to β-turn structures. Scanning electron microscopy results suggested that PLPs contributed to the orderly and uniform appearance of MPs. Furthermore, molecular docking analysis elucidated that arginine, glutamic acid, aspartic acid and glutamine were the predominant binding sites between MPs and the phenolic compounds of PLPs. Consequently, PLPs could be employed as a potential natural antioxidant to alleviate oxidation and modify the functional characteristics of meat products.

Autonomous Robotic System to Prune Sweet Pepper Leaves Using Semantic Segmentation with Deep Learning and Articulated Manipulator.

This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the articulated manipulator. A semantic segmentation neural network is employed to recognize the different parts of the sweet pepper plant, which is then used to create 3D point clouds for detecting the pruning position and the manipulator pose. Eventually, a manipulator robot is controlled to prune the crop part. This article provides a detailed description of the three tasks involved in building the sweet pepper pruning system and how to integrate them. In the experiments, we used a robot arm to manipulate the pruning leaf actions within a certain height range and a depth camera to obtain 3D point clouds. The control program was developed in different modules using various programming languages running on the ROS (Robot Operating System).

Early stage black pepper leaf disease prediction based on transfer learning using ConvNets

Plants get exposed to diseases, insects and fungus. This causes heavy damages to crop resulting in various leaves diseases. Leaf diseases can be diagnosed at an early stage with the aid of a smart computer vision system and timely disease prevention can be targeted. Black pepper is a medicinal plant that is extensively used in Ayurvedic medicine because of its therapeutic properties. The proposed work represents an intelligent transfer learning technique through state-of-the-art deep learning implementation using convolutional neural network to predict the presence of prominent diseases in black pepper leaves. The ImageNet dataset available online is used for training deep neural network. Later, this trained network is utilized for the prediction of the newly developed black pepper leaf image dataset. The developed data set consist of real time leaf images, which are candidly taken from the fields and annotated under supervision of an expert. The leaf diseases considered are anthracnose, slow wilt, early stage phytophthora, phytophthora and yellowing. The hyperparameters chosen for tuning in to deep learning models are initial learning rates, optimization algorithm, image batches, epochs, validation and training data, etc. The accuracy obtained with 0.001 learning rate ranges from 99.1 to 99.7% for the Inception V3, GoogleNet, SqueezeNet and Resnet18 models. Proposed Resnet18 model outperforms all model with 99.67% accuracy. The resulting validation accuracy obtained using these models is high and the validation loss is low. This work represents improvement in agriculture and a cutting edge deep neural network method for early stage leaf disease identification and prediction. This is an approach using a deep learning network to predict early stage black pepper leaf diseases.

Population Genomics Reveals an Emerging Lineage of Xanthomonas perforans on Pepper.

Xanthomonas perforans-the dominant causal agent of bacterial leaf spot of tomato-is an emerging pathogen of pepper, indicative of a potential host expansion across the southeastern United States. However, studies of the genetic diversity and evolution of X. perforans from pepper remain limited. In this study, the whole-genome sequences of 35 X. perforans strains isolated from pepper from four fields and two transplant facilities across southwest Florida between 2019 and 2021 were used to compare genomic divergence, evolution, and variation in type III secreted effectors. Phylogenetic analysis based on core genes revealed that all 35 X. perforans strains formed one genetic cluster with pepper and tomato strains from Alabama and Turkey and were closely related to strains isolated from tomato in Indiana, Mexico, and Louisiana. The in planta population growth of tomato strains isolated from Indiana, Mexico, Louisiana, and Turkey in pepper leaf mesophyll was on par with pepper X. perforans and X. euvesicatoria strains. Molecular clock analysis of the 35 Florida strains dated their emergence to approximately 2017. While strains varied in copper tolerance, all sequenced strains harbored the avrHah1 transcription activation-like effector located on a conjugative plasmid, not previously reported in Florida. Our findings suggest that there is a geographically distributed lineage of X. perforans strains on tomato that has the genetic background to cause disease on pepper. Moreover, this study clarifies potential adaptive variants of X. perforans on pepper that could help forecast the emergence of such strains and enable immediate or preemptive intervention.

Bayesian optimization with deep learning based pepper leaf disease detection for decision-making in the agricultural sector

&lt;abstract&gt; &lt;p&gt;Agricultural decision-making involves a complex process of choosing strategies and options to enhance resource utilization, overall productivity, and farming practices. Agricultural stakeholders and farmers regularly make decisions at various levels of the farm cycle, ranging from crop selection and planting to harvesting and marketing. In agriculture, where crop health has played a central role in economic and yield outcomes, incorporating deep learning (DL) techniques has developed as a transformative force for the decision-making process. DL techniques, with their capability to discern subtle variations and complex patterns in plant health, empower agricultural experts and farmers to make informed decisions based on data-driven, real-time insights. Thus, we presented a Bayesian optimizer with deep learning based pepper leaf disease detection for decision making (BODL-PLDDM) approach in the agricultural sector. The BODL-PLDDM technique aimed to identify the healthy and bacterial spot pepper leaf disease. Primarily, the BODL-PLDDM technique involved a Wiener filtering (WF) approach for pre-processing. Besides, the complex and intrinsic feature patterns could be extracted using the Inception v3 model. Also, the Bayesian optimization (BO) algorithm was used for the optimal hyperparameter selection process. For disease detection, a crayfish optimization algorithm (COA) with a long short-term memory (LSTM) method was employed to identify the precise presence of pepper leaf diseases. The experimentation validation of the BODL-PLDDM system was verified using the Plant Village dataset. The obtained outcomes underlined the promising detection results of the BODL-PLDDM technique over other existing methods.&lt;/p&gt; &lt;/abstract&gt;

Farmers’ Perception of Viral Diseases and Their Management in Pepper (Capsicum spp.) Production in Benin

Pepper (Capsicum spp.) is an important solanaceous cash crop in Benin; however, productivity is limited due to several key constraints, especially diseases caused by viruses. We sought to understand farmers’ perceptions of viral diseases, management strategies deployed, and to identify the virus population affecting pepper production in Benin. To assess farmers’ perceptions and management of viral diseases, a survey was carried out in four agroecological zones of Benin. A total of 144 pepper farmers were interviewed using the snowball method. A total of 52 pepper leaf samples with virus-like symptoms were collected and diagnosed by reverse-transcriptase polymerase chain reaction (RT-PCR) or PCR. Pepper production systems varied across agroecological zones (P ≤ 0.001) with a predominance of farms practicing monoculture (82%). The majority of farmers (89%) indicated that pests and diseases were the main constraints to increased production. Cucumber mosaic virus (92% of the total samples), Pepper vein yellow virus (52%), and Pepper veinal mottle virus (50%) were the major viruses detected in pepper fields in Benin. There were both single (29%) and mixed (71%) infections of the viruses, suggesting that mixed infections are common for pepper in Benin, confounding efforts to reduce virus infections. Nearly 100% of the farmers surveyed were not aware of these viral diseases. They also could not directly relate symptoms of virus infection to the presence of aphids, whiteflies, or thrips. Farmers relied primarily on synthetic insecticides (93%) to control virus vectors. Interestingly, some farmers applied commercial (12%) and homemade (17%) biopesticides, with neem-based preparations being the most widely used. A total of 15% of farmers used companion cropping with maize, mint or basil and 43% of farmers used crop rotation as a cultural management practice to control viral disease and vector pressure in pepper fields. The implications of this work include the importance of training farmers and extension agents on diagnosis of viruses and their vectors causing viral diseases. This study provides baseline information for the development of host-resistant cultivars and deployment of integrated pest management strategies for pepper in Benin to reduce farmer losses.

Early Disease Detection in Plants using CNN

The Indian economy benefits from early disease detection in plant leaves. According to reports, 10-30% of crops suffer harm from diseases that are not discovered during the curing process. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have paved the way for transformative solutions in disease identification for agriculture. This study addresses the critical issue of early disease detection by harnessing the power of deep learning models, specifically CNNs. Different leaf disease detection technologies are used for different crops. The pre-trained deep learning model is used in this study to identify and categorize leaf diseases. A dataset of tomato, potato, and bell pepper leaf pictures from the plant village repository was employed for the current investigation. The developed model can detect 12 plant diseases in normal leaf tissue. The quantitative assessment of our CNN-based technique reveals an impressive accuracy rate of 86.21%. This notable accuracy underscores the efficacy of our approach in the challenging domain of plant disease detection. Our findings show potential for the larger agricultural environment beyond the immediate quantitative advantages. This technical advance not only paves the way for improved crop output and lower losses, but also brings in a new era of data-driven sustainability in agriculture. The bottom line is that our research "offers a tangible pathway for leveraging AI-powered solutions to address the long-standing challenges of plant disease detection, thereby significantly contributing to the well-being of farmers and the sustenance of the Indian agricultural sector.

Induced Resistance Mechanism of Bacillus velezensis S3-1 Against Pepper Wilt.

In recent years, pepper wilt has emerged as a pivotal constraint on pepper yield augmentation. Bacillus velezensis S3-1, with a wide array of hosts, can be used as both a biocontrol agent and biofertilizer. Nonetheless, the precise mechanisms underpinning its employment in combating pepper wilt remain cloaked in ambiguity. In our study, we found that B. velezensis S3-1 could significantly inhibit Fusarium sp. F1T that caused pepper wilt. S3-1 could effectively inhibit both the growth and germination of F1T conidia, leading to a reduction in the spore germination percentage from 83.2 to 37.1% in vitro experiments. Additionally, leaf detachment experiments revealed that the volatile compounds produced by S3-1 could inhibit the spread of pepper leaf spot area. Moreover, we observed a significant decrease in the content of malondialdehyde (MDA) in pepper treated with S3-1, along with a significant increase in the content of soluble protein, polyphenol oxidase (PPO), peroxidase (POD), and phenylalanine ammonia-lyase (PAL) in pepper. Furthermore, RT-PCR analysis showed that the expression of the defense genes CaPR 1 and CaPIN II in pepper after treatment with S3-1 was significantly upregulated, suggesting that S3-1 had the potential to induce systemic resistance in pepper, thereby enhancing its disease resistance. Hence, our findings suggest that S3-1 can be a promising biocontrol agent for managing pepper wilt in modern agriculture.

Integrated transcriptome and metabolome analysis reveals the mechanism of carotenoid regulation in the yellowing-leaf mutant of pepper (Capsicum annuum L.) in response to different temperatures

Temperature is an important environmental factor affecting plant color, and different temperatures have distinct effects on plant leaf color changes. In order to explore the effects of different temperatures on plant color, we measured the physiological characteristics, transcriptome, and metabolome of the pepper leaf yellowing mutant yl1 and its wild type 6421 under different temperature conditions. Results showed that high temperature treatment compared with the normal temperature treatment significantly increased the contents of chlorophyll a, chlorophyll b, and carotenoids in yl1. At the same time, a total of 28 carotenoid pathway metabolites were detected under three temperature conditions. Among them, β-carotene, zeaxanthin, violaxanthin, neoxanthin, lutein, and antheraxanthin were higher in pepper leaves. The expression levels of ZEP, bHLH104, MYB1R1, and MYB1R1-like were significantly increased under high temperature compared with under normal temperature, while the expression level of crtZ-2 and VDE were significantly decreased. Co-expression network analysis showed that VDE was positively correlated with the bHLH71-like and negatively correlated with MYB1R1-like and bHLH104. These results led to significant differences in the amount of zeaxanthin and antheraxanthin accumulated at different temperatures, which may be the reason for the color change of pepper leaves. However, under the normal and low temperature treatment, it is the opposite, resulting in leaf yellowing. This study provides a reference for further research on the mechanism underlying the impact of temperature on carotenoid metabolism in pepper leaves.

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Agricultural products are vital to the sustainability of the economies of developing countries. Most developing countries’ economies such as Ethiopia heavily rely on agriculture. On a global scale, the pepper crop is one of the most important agricultural products in terms of human food security. However, it is susceptible to a variety of diseases which include blight leaf disease, gray leaf spot, common rust, fruit rot disease, powdery mildew symptoms on pepper leaf, and other related diseases that are all common today. Currently, more than 34 different pepper diseases have been discovered, resulting in a 33% average yield loss in pepper cultivation. Conventionally, farmers detect the disease using visual observation but this has its own demerits as it is usually not accurate and usually time consuming. In the past, a number of researchers have presented various methods for classifying pepper plant disease, especially using image processing and deep learning techniques. However, earlier studies have shown that binary classification requires improvement as some classes were more challenging to identify than others. In this study, we propose a concatenated neural network of the extracted features of VGG16 and AlexNet networks to develop a pepper disease classification model using fully connected layers. The development of the proposed concatenated CNN model includes steps such as dataset collection, image preprocessing, noise removal, segmentation, feature extraction, and classification. Finally, the proposed concatenated CNN model was evaluated, providing a training classification accuracy of 100%, validation accuracy of 97.29%, and testing accuracy of 95.82%. In general, it can be concluded from the findings of the study that the proposed concatenated model is suitable for identifying pepper leaf and fruit diseases from digital images of pepper.

Genetic analysis and QTLs identification for resistance to the Begomovirus causing pepper leaf curl virus (PepLCV) disease

Genetic analysis and QTLs identification for resistance to the Begomovirus causing pepper leaf curl virus (PepLCV) disease