Fruit Disease Research Articles

A Deep Learning Approach for Detection of Disease and Classification of Fruits

The calorie component of fruit is identified using a common photo dataset and several machine learning advancements, including Pre-processing, segmentation, function extraction, and classification which is primarily based on size and shape. Using image processing methods, the dimensions of fruit objects are calculated. Finally, we'll provide users and patients with the best advice for fruit consumption based on our best estimates of the number of calories in the fruit. To help farmers save time and money while improving the accuracy of plant disease detection, we have developed a technology. Thanks to the usage of many speed-enhancing methods, it outperforms competing systems. An easy-to- implement method for picture segmentation, classification, and reconstruction, the SVM Algorithm is what we're employing here. Even when the objective function isn't acting well, it may nevertheless perform well during global optimization.

Detection of fruit tree diseases in natural environments: A novel approach based on stereo camera and deep learning

The occurrence of diseases in orchards has a significant impact on fruit yield and quality. Inspection devices equipped with cameras can effectively replace manual intervention in the process of orchard management by swiftly detecting diseases. However, the images captured by such devices often exhibit a wide vision field and contain a significant amount of extraneous information. This paper presented a method for detecting diseases in natural environments based on binocular cameras and deep learning techniques applied to fruit tree leaf images with a wide visual field. Firstly, the ZED2i binocular camera was utilized to capture image pairs from a long distance, simulating the visual field of an inspection device. These image pairs were then processed using the Unimatch stereo matching algorithm to obtain a disparity map and calculated the corresponding depth map. The depth information was used to create a mask, eliminating irrelevant background information from the images. Secondly, a lightweight disease detection (LDD) model was proposed based on the advanced YOLOv5 framework for detecting pear rust and plum perforation diseases. The backbone network consisted of shuffle channel block, inverted shuffle channel block, and convolutional block attention module, with only one detection head used in the classifier part. The final experiments evaluated the segmentation, model improvement, and disease spots detection performance. The results showed that the depth map obtained using Unimatch for stereo matching was more accurate than that obtained using the ZED software development kit. In ablation experiments, LDD achieved a mean average precision of 93.0%, with a model size of only 3.9 MB, outperforming the original YOLOv5-s model. Preprocessed images with depth information exhibited improved detection performance, achieved a F1 score of 93.62%, which was a 10.92% improvement over direct detection of the original images. Overall, the presented method successfully addresses the issue of background interference when detecting diseases of fruit tree leaf with a wide visual field, providing a technical basis for automated orchard inspection operations.

First Report of Mango Stem-end Rot Caused by Pestalotiopsis kenyana in Yunnan Province, China.

Stem End Rot (SER) is a devastating post-harvest disease of mango fruits causing severe losses during storage. In 22 July 2023, 31 out of 50 intact mangoes (cv. Sensation) collected from five orchards in Huaping county (26°37'N 101°15') showed typical symptoms of SER after stored for 9 d in room temperature (24-28℃). Initially, small dark brown to black spots appeared around the fruit peduncle, which rapidly expanded through the pulp tissues. The symptomatic mangoes were surface disinfected by 3% NaClO for 30 s after soaking in 75% alcohol for 3 min, and cleaned by sterile water for 3 times. Tissues were cut from the edge of lesions, dried by sterile filter paper, transferred to PDA and cultured at 28 ℃ for 5 d (Tovar-Pedraza et al., 2020). The single-spore isolation method was used to obtain pure culture. Thirty eight isolates presented four distinct kind of morphology on PDA medium. Among them, 11 isolates with same morphology were significantly distinct from common pathogens of SER. The colonies were white and pale yellow on reverse side. Mycelia grew fast and reached the edge of 90 mm Petri dish after cultured for 5d. Pycnidia were black and scattered on the mycelial mats after 15-20 d. Conidia were fusoid, straight to slightly curved, four septa, and brown. Pigmented median cells doliiform, 14.97 - 18.62(16.11 ±0.89)×5.61- 7.28 (6.61±0.51) μm. Apical cell hyaline, subcylindrical; 1-3 tubular transparent apical appendages 12.27 - 16.68 (13.65±3.78)×1.14 - 1.99 (1.59±0.36) μm. Basal cell conical with a truncate base, hyaline, and 1-2 tubulose basal appendages with 2.85 - 7.97 (5.18±1.88)×0.99 - 1.85 (1.38±0.29) μm (n=50). These fungi were described as Pestalotiopsis kenyana. based on morphological characters (Maharachchikumbura et al., 2014) which were different from isolates characterized as other common SER pathogens (Botryosphaeria, Neofusicoccum). Based on morphology, HPSX-4 was selected for further identification. ITS region, tef1-α, β-tub of HPSX-4 were amplified and sequenced (Xun et al., 2023). The sequences were deposited in GenBank (ITS:OR889126, tef1-α:OR913431, β-tub: OR913432). The ITS, tef1-α, β-tub sequence of HPSX-4 showed 100% (525/525)，99.59% (241/242), and 100% (742/742) identity to the P. kenyana CBS442.67 sequences (ITS: NR147549，tef1-α: KM199502, β-tub: KM199395), respectively. HPSX-4 clustered with P. kenyana CBS 442.67 (type strain) based on maximum likelihood method by MEGA 7.0.21(Minh et al., 2013). Pathogenicity test was performed on 12 healthy mangoes (cv. Golek) by placing mycelial plugs around the peduncle and the middle of the fruit by pin-prick method according to Feng et al.(2023). Sterile PDA were used as control (three mangoes). Every inoculated fruit was incubated at 28°C, 95% ± 3% humidity with three replicates for each treatment. The experiment was repeated three times. Typical symptoms of SER were observed. There were no symptoms in the control group. The strain was reisolated and identified as P. kenyana with the method mentioned above which fulfilled Koch's postulates. This is the first report of P. kenyana causing SER disease on Mangifera indica L.. This study expands our understanding of the pathogen range of mango SER which conducive to prevent and control the SER caused by P. kenyana.

Regulation of sucrose metabolism, sugar transport and pentose phosphate pathway by PacC in apple fruit colonized by Penicillium expansum

A critical transcription factor, PacC, modulates the expression of fungal pH signaling. Although PacC-mediated environmental pH has been reported to regulate the growth and pathogenicity of postharvest pathogens, the involvement of PacC in sucrose metabolism, sugar transport, and the pentose phosphate pathway (PPP) in different zones of decayed fruit remains unclear. Our work showed that the inoculation with a PePacC deletion strain of Penicillium expansum (ΔPePacC) accelerated sucrose catabolism and glucose and fructose accumulation in different zones of apple fruit. This was attributed to an increase in sucrose metabolism enzyme activities and up-regulation of the sugar transporter protein-related gene expression. Moreover, ΔPePacC inoculation increased the PPP-related enzyme activities and the levels of nicotinamide adenine dinucleotide phosphate (NADPH) and NADP+. In conclusion, PacC modulates sucrose metabolism, sugar transport, and the PPP in apple fruit by mediating dynamic changes in environmental pH, thereby enhancing fruit disease resistance.

Electrolyzed Salt Solutions Used against Major Postharvest Diseases of Fresh Fruit and Vegetables.

Alternative means of control are becoming increasingly relevant to the improvement of safety and the reduction of postharvest losses and waste of fruit and vegetables, especially in view of the application of the EU Greed Deal. A previous study from our research group that focused on the electrolysis process of water and was conducted using NaCl and NaHCO3 as electrolytes proved to efficiently reduce pathogen inoculum in packinghouse washing water. In the present study, we examined the effect of the electrolyzed salt solutions (eNaCl and eNaHCO3) produced in the same experimental conditions previously reported to be used as postharvest treatments during handling and commercialization, and/or at the consumer's site. We tested the electrolyzed solutions, obtained in the presence or absence of the salts, against five relevant fungal pathogens in terms of conidia viability, and on various hosts in terms of rot incidence/severity. Chemical parameters of electrolyzed and non-electrolyzed solutions were also assessed. Although a different susceptibility to treatments was observed among pathogens, electrolyzed sodium chloride (eNaCl) was the most efficient treatment for preventing spore germination, as well as for minimizing fruit rot. However, a consistent control of fungal viability and consequent rot was also achieved using electrolyzed tap water (eW). The eNaHCO3, although less efficient on fungal viability, provided a significant effect against fruit rot. The investigated electrolyzed solutions seem promising for reducing the waste of fresh fruit and vegetables.

Managing fruit rot diseases of Vaccinium corymbosum.

Blueberry is an important perennial fruit crop with expanding consumption and production worldwide. Consumer demand for blueberries has grown due to the desirable flavor and numerous health benefits, and fresh market production in the U.S. has risen in turn. U.S. imports have also increased to satisfy year-round consumer demand for fresh blueberries. Pre- and post-harvest fruit diseases such as anthracnose (caused by Colletotrichum spp.) and botrytis fruit rot (caused by Botrytis spp.) have a significant impact on fruit quality and consumer acceptance. These are also among the most difficult diseases to control in the blueberry cropping system. These latent pathogens can cause significant losses both in the field, and especially during transport and marketplace storage. Although both diseases result in rotted fruit, the biology and infection strategies of the causal pathogens are very different, and the management strategies differ. Innovations for management, such as improved molecular detection assays for fungicide resistance, postharvest imaging, breeding resistant cultivars, and biopesticides have been developed for improved fruit quality. Development and integration of new strategies is critical for the long-term success of the blueberry industry.

Efficient fruit disease diagnosis on resource-constrained agriculture devices

Climate change in recent years has caught the attention of researchers worldwide, with drastic effects on many economic sectors, especially agriculture and livestock. Due to global warming and declining water levels, food security is becoming an increasingly urgent concern. The situation is more challenging in underdeveloped nations such as Pakistan, where little attention has been paid to improving the processes to produce effective seeds and herbicides. The problem has been exacerbated due to financial constraints and a lack of technology to monitor crops. It has been observed that even a noncritical disease can ruin the crop if timely inspections aren’t performed. Although many solutions have been proposed recently, most are based on heavy mobile applications and cloud-based solutions, which results in considerable computational cost, power consumption, and latency. This work proposes a Deep Neural Networks (DNNs)-based light-weight solution for diagnosing and classifying apple crop diseases for farmers working in rural places (where the internet is not accessible). With the increasing need for efficient and robust Machine Learning (ML) applications on edge devices, such as mobile phones, Raspberry Pi, and Jetson Nano-based devices, there has been a demand for more efficient Deep Neural Networks (DNN) models. To meet this demand, various DNN-based models have been experimented with, including Basic CNN Architecture, AlexNet, and EfficientNet Lite to measure performance After a thorough examination and evaluation of each model’s performance, efficiency, and resource usage, the most optimal model was picked and developed applications for classifying apple crop disease. Using a transfer learning strategy on a specially developed EfficientNet DNN architecture, achieving 85% test accuracy.

Assessment of Genetic Diversity and Relatedness of Local Banana (Musa spp.) Cultivars in Malaysia Using Simple Sequence Repeat (SSR) Markers

Banana (Musa spp.) is an economically important crop widely consumed all over the world. Understanding the genetic diversity and relatedness between various banana cultivars is important to continuously improve its fruit characteristics, nutritional value, and disease resistance. This study aimed to develop a DNA fingerprinting panel using polymorphic simple sequence repeat (SSR) markers for the genetic characterisation of 11 banana cultivars in Malaysia. A total of 10 polymorphic SSR markers were developed into a multiplex PCR reaction and capillary electrophoresis to uniquely profile our collection of banana cultivars. The developed fingerprinting panel successfully amplified a total of 95 alleles, with 6 to 13 alleles per SSR marker. The average SSR marker polymorphic information content (PIC) value is 0.812, indicating the informativeness of the panel. Analysis of molecular variance (AMOVA) shows that 97% of the molecular variance from our banana collection is due to inter-variety genetic diversity, while the remaining 3% is due to intra-variety genetic diversity. A population structure analysis groups our collection of banana varieties according to the presence of at least one M. balbisiana (B) genome in their genetic makeup. The Cavendish variety, however, showed a distinct structure compared to the other cultivars. This SSR fingerprinting panel provides valuable insights into the genetic diversity and relatedness between banana cultivars in Malaysia. It has the potential to assist future banana breeding initiatives and serve as an effective quality control measure for verifying varieties in a tissue culture facility involved in banana planting materials production.

E3 ligase SlCOP1-1 stabilizes transcription factor SlOpaque2 and enhances fruit resistance to Botrytis cinerea in tomato.

CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1), a pivotal repressor in plant photomorphogenesis, has been extensively studied in various plant processes. However, the specific roles of COP1 in fruit remain poorly understood. Here, we functionally characterized SlCOP1-1 (also known as LeCOP1), an Arabidopsis (Arabidopsis thaliana) COP1 ortholog, in tomato (Solanum lycopersicum) fruit ripening and disease resistance. Despite the clear upregulation of SlCOP1-1 during fruit ripening, knockout or overexpression of SlCOP1-1 in tomatoes only minimally affected ripening. Intriguingly, these genetic manipulations substantially altered fruit resistance to the fungal pathogen Botrytis cinerea. Proteomic analysis revealed differential accumulation of proteins associated with fruit disease resistance upon SlCOP1-1 knockout or overexpression. To unravel the mechanism of SlCOP1-1 in disease resistance, we conducted a screen for SlCOP1-1-interacting proteins and identified the stress-related bZIP transcription factor SlOpaque2. We provide evidence that SlOpaque2 functions in tomato resistance to B. cinerea, and SlCOP1-1-mediated mono-ubiquitination and stabilization of SlOpaque2 contributes to fruit resistance against B. cinerea. Our findings uncover a regulatory role of COP1 in controlling fruit disease resistance, enriching our understanding of the regulatory network orchestrating fruit responses to disease.

Effectiveness of Fludioxonil, a New-Generation Reduced-Risk Fungicide, Against Brown Rot Pathogens.

Brown rot, caused by Monilinia species, is a destructive disease of pome and stone fruits that can lead to significant losses in production. Disease management is mainly based on fungicide applications during the growing season. Fludioxonil, a "new-generation reduced-risk fungicide", is one of the most important fungicide used. The objectives of the present study were to compare and determine the toxicity of fludioxonil to selected M. laxa, M. fructigena and M. fructicola isolates, to test its effectiveness in detached fruits and to assess its effectiveness under practical control conditions. A total of 27 isolates (10 isolates of M. laxa, 8 of M. fructigena and 9 of M. fructicola) were tested for sensitivity to fludioxonil in vitro. Isolates from each species exhibited a homogeneous response to the fungicide, while differences among the different species were determined. Based on calculated resistance factors (RF), the examined isolates were classified into two categories: sensitive and moderately resistant. In vivo testing of the effectiveness of the label concentration of fludioxonil on detached fruit did not reveal differences between isolates classified into different sensitivity categories; fludioxonil used at the label concentration (0.1%) inhibited decay development 93.5 to 100%, regardless of the isolate category. Field trials revealed the very high efficacy of fludioxonil in preventing brown rot on fruits, ranging from 92.2 to 100 for peach, 90.7 to 97.3 for plum and 84.9 to 91.9% for sour cherry. In conclusion, fludioxonil was highly effective according to in vitro sensitivity tests and when used under practical field conditions for brown rot control.

Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit

Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations.

Deep Learning based Apple Fruit Disease Detection using Dense Net Recursive Convolutional Neural Network (DNRCNN)

Every year, fruit diseases cost the apple industry a lot of money. It can be challenging for growers to identify various apple infections because the symptoms of various illnesses are often similar and may overlap. In this study, we suggest a deep learning-based method for identifying and categorizing apple diseases. Dataset generation, which includes data collection and data labelling, is the first stage of the investigation. On the prepared dataset, we then trained a deep learning-based Dense Net Recurrent Convolutional Neural Network (DNRCNN) model for automatically classifying apple diseases. The end-to-end learning algorithm DNRCNN is appropriate for a range of tasks including image classification, object detection, and segmentation because it automatically extracts complex features from source images and learns them directly. Initialize the parameters of the proposed deep model using transfer learning. To avoid over-fitting, data augmentation techniques like rotation, translation, reflection, and scaling are also used. On the prepared dataset, the proposed Dense Net Recursive Convolutional Neural Network (DNRCNN) model achieves promising results, with an accuracy of about 96%. Some of the intricate and helpful image characteristics for detection are captured by the suggested model for classification. The model can learn the higher-order features of two adjacent layers that are not in the same channel but have a high correlation more effectively than existing techniques. High training and validation accuracy have been achieved when training and validating the suggested model. The findings support the method's usefulness in categorizing different apple diseases and show that it can be useful for farmers

Effect of Magnetic Field and UV-C Radiation on Postharvest Fruit Properties

This review focuses on the recent information on the effect of different types of magnetic fields (MFs) and ultraviolet radiation (UV-C) on the processes that may finally affect fruit quality and its storage potential. Firstly, the biological effect of MFs on every plant’s growth and development level is described. The magnetic field interacts with a plant’s metabolism and changes the permeability of membranes affecting cells’ homeostasis. It also could affect early seedling development, stimulating enzyme activity and protein synthesis, and later on nutrient and water uptake of adult plants. In some cases, it makes plants more resilient, increasing their tolerance to environmental stresses. Also, MF treatment could lower the disease index of plants, thus improving the internal and external fruit quality indices. The second part of this review focuses on interesting perspectives of using UV-C radiation to reduce postharvest fruit diseases, but also to delay fruit ripening and senescence. The application of UV-C light to combat postharvest infections is associated with two mechanisms of action, such as direct elimination of microorganisms located on the fruit surface and indirect triggering of the plant’s defense reaction. Moreover, the use of hormetic doses of UV-C can additionally increase the nutritional properties of fresh fruit, lead to the accumulation of desired phytochemicals such as polyphenols, for example, to increase anthocyanin or resveratrol content, or elevate antioxidant activity.

Postharvest biocontrol ability and involved mechanism of volatile organic compounds from Serratia nematodiphila BC-SKRU-1 against Penicillium digitatum tangerine fruit

Volatile organic compounds (VOCs) emitted by microorganisms offer a promising strategy for managing green mold disease in citrus fruits caused by Penicillium digitatum. This study evaluated the biocontrol efficacy of VOCs produced by Serratia nematodiphila BC-SKRU-1 (BC-SKRU-1 VOCs) against green mold in postharvest tangerine fruits, targeting P. digitatum NKP4321. Our findings demonstrated that BC-SKRU-1 VOCs exhibited a broad spectrum of activity against seven plant pathogens, with the highest efficacy observed against NKP4321 (68.21%). A concentration of 106 CFU mL−1 for VOC production by BC-SKRU-1 completely inhibited the mycelial growth of NKP4321, whereas a concentration of 107 CFU mL−1 was required to fully inhibit spore germination. The application of four plates of BC-SKRU-1 VOCs at 108 CFU mL−1 significantly reduced the severity of green mold disease, with optimal biocontrol achieved through continuous fumigation during storage. Notably, 2-nonanol, identified as the predominant component among the 20 VOCs using HS-SPME/GC-MS, exhibited superior antifungal activity both in vitro and in vivo. It effectively decreased ergosterol production and disrupted plasma membrane integrity in NKP4321 cells. Furthermore, 2-nonanol altered the cellular content of non-enzymatic antioxidants (e.g., GSH, GSSG) and enzymatic antioxidants (e.g., CAT, SOD), elucidating its comprehensive antifungal mechanism. These findings highlight the potential of BC-SKRU-1 VOCs, particularly 2-nonanol, as effective biocontrol agents for enhancing postharvest fruit preservation by combating fungal pathogens.

MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks

ABSTRACT Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system’s model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.

Gene function characterization in Aspergillus niger using a dual resistance marker transformation system mediated by Agrobacterium tumefaciens

Aspergillus niger is a well-known workhorse for the industrial production of enzymes and organic acids. This fungus can also cause postharvest diseases in fruits. Although Agrobacterium tumefaciens-mediated transformation (ATMT) based on antibiotic resistance markers has been effectively exploited for inspecting functions of target genes in wild-type fungi, it still needs to be further improved in A. niger. In the present study, we re-examined the ATMT in the wild-type A. niger strains using the hygromycin resistance marker and introduced the nourseothricin resistance gene as a new selection marker for this fungus. Unexpectedly, our results revealed that the ATMT method using the resistance markers in A. niger led to numerous small colonies as false-positive transformants on transformation plates. Using the top agar overlay technique to restrict false positive colonies, a transformation efficiency of 87 ± 18 true transformants could be achieved for 106 conidia. With two different selection markers, we could perform both the deletion and complementation of a target gene in a single wild-type A. niger strain. Our results also indicated that two key regulatory genes (laeA and veA) of the velvet complex are required for A. niger to infect apple fruits. Notably, we demonstrated for the first time that a laeA homologous gene from the citrus postharvest pathogen Penicillium digitatum was able to restore the acidification ability and pathogenicity of the A. niger ΔlaeA mutant. The dual resistance marker ATMT system from our work represents an improved genetic tool for gene function characterization in A. niger.

SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction

SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction

Comparative Transcriptome Analysis of Different Mulberry Varieties to Reveal Candidate Genes and Small Secreted Peptides Involved in the Sclerotiniose Response

Mulberry sclerotiniose is a devastating fungal disease of mulberry fruit and has been a limitation for the utility of mulberry fruits and the diversified development of sericulture. In the present study, we presented a workflow for screening candidate sclerotiniose-resistance genes and small secreted peptides (SSPs) based on a genome-wide annotation of SSPs and comparative transcriptome analysis of different mulberry varieties. A total of 1088 SSPs with expression evidence were identified and annotated in mulberry. A comprehensive analysis of the sclerotiniose-related RNA sequencing datasets showed that photosynthesis, plant hormone signaling, and metabolic pathways were the main pathways involved in the response to sclerotiniose. Fifty-two candidate sclerotiniose-response genes (SRGs), including 15 SSPs, were identified based on comparative transcriptome analysis. These SRGs are mainly involved in the hormone signaling pathway and cell wall biogenesis. Transient overexpression in tobacco and the knock-down of five SRGs affected the resistance against Ciboria shiraiana. MaMYB29, MaMES17, and MaSSP15 were primarily determined as negative regulators of plant resistance to C. shiraiana infection. Our results provide a foundation for controlling sclerotiniose in mulberry using genetic engineering and biological approaches such as spraying antifungal peptides.

Prevalencia e infección mixta de virus en cultivos de maracuyá amarillo (Passiflora edulis f. flavicarpa) en el Valle del Cauca, Colombia

Yellow passion fruit crop is affected by multiple phytosanitary problems. Among the main ones are viruses of the genera Potyvirus (soybean mosaic virus, SMV), Cucumovirus (cucumber mosaic virus, CMV), Tymovirus (tymovirus passiflorae, PYMV) and Begomovirus (passionfruit leaf distortion virus, PLDV). Information about the prevalence and interaction between these viruses is scarce. The objective of this study was to verify the prevalence and identify the types of mixed viral infections of potyvirus, cucumovirus, tymovirus, and begomovirus in passion fruit crops in Valle del Cauca, Colombia. Passion fruit leaf samples with symptoms were collected and their nucleic acids were purified. Virus identification was performed by PCR using universal primers by viral genus, and specific primers for the SMV, CMV, PFYMV and PLDV viruses. The incidence of each virus by municipality was analyzed and the severity of the symptoms was compared with the diagnosis made. In total, 66 samples were collected in the municipalities of Toro, Dagua, Roldanillo, Bolívar, La Unión and El Cerrito. It was established that the most prevalent viruses are PLDV followed by SMV and CMV. The presence of PYMV was not detected in the samples analyzed. The presence of double and triple infections among potyvirus, cucumovirus and begomovirus were identified for the first time, finding greater severity of symptoms with a greater number of viruses identified per sample. This research provides key results for the design of control strategies for viral diseases in passion fruit.

Advancements and applications of loop‐mediated isothermal amplification (LAMP) technology in the detection of viral diseases in fruit crops

AbstractEarly diagnosis has a pivotal role in the effective control and prevention of disease in fruit crops. Loop‐mediated isothermal amplification (LAMP) has emerged as a prominent modality for disease diagnosis owing to its rapid detection, heightened sensitivity, exceptional specificity and easy operational procedures. The advent of LAMP‐coupled CRISPR‐Cas12, multiplex LAMP (mLAMP) and digital LAMP (dLAMP) has contributed to a paradigm shift in the field and increased the applicability of LAMP technology in viral disease detection. Such advancements have further extended the reach and efficacy of LAMP methodologies in diagnostic research. Here, we review LAMP detection technology application to identify viral diseases in fruits (e.g., apple, passion fruit, banana and citrus) and summarize current developments in LAMP technology and its future prospects, which could enhance its application in the detection of fruit viral diseases.