Strawberry Disease Research Articles

Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm.

BHI-YOLO: A Lightweight Instance Segmentation Model for Strawberry Diseases

In complex environments, strawberry disease segmentation models face challenges, such as segmentation difficulties, excessive parameters, and high computational loads, making it difficult for these models to run effectively on devices with limited computational resources. To address the need for efficient running on low-power devices while ensuring effective disease segmentation in complex scenarios, this paper proposes BHI-YOLO, a lightweight instance segmentation model based on YOLOv8n-seg. First, the Universal Inverted Bottleneck (UIB) module is integrated into the backbone network and merged with the C2f module to create the C2f_UIB module; this approach reduces the parameter count while expanding the receptive field. Second, the HS-FPN is introduced to further reduce the parameter count and enhance the model’s ability to fuse features across different levels. Finally, by integrating the Inverted Residual Mobile Block (iRMB) with EMA to design the iRMA, the model is capable of efficiently combining global information to enhance local information. The experimental results demonstrate that the enhanced instance segmentation model for strawberry diseases achieved a mean average precision (mAP@50) of 93%. Compared to YOLOv8, which saw a 2.3% increase in mask mAP, the improved model reduced parameters by 47%, GFLOPs by 20%, and model size by 44.1%, achieving a relatively excellent lightweight effect. This study combines lightweight architecture with enhanced feature fusion, making the model more suitable for deployment on mobile devices, and provides a reference guide for strawberry disease segmentation applications in agricultural environments.

Green Alternatives for the Control of Fungal Diseases in Strawberry: In-Field Optimization of the Use of Elicitors, Botanical Extracts and Essential Oils

Finding safe and reliable alternatives to fungicides is currently one of the biggest challenges in agriculture. In this regard, this experiment investigated the effectiveness of different elicitors, botanical extracts and essential oils to control grey mold (Botrytis cinerea) and powdery mildew (Podosphaera aphanis) on strawberry plants. This trial was conducted in field conditions under a plastic tunnel with strawberry plants ‘Elsanta’. A first group of strawberry plants was treated before flowering with elicitors [acibenzolar-S-Methyl–(BTH), chitosan], botanical extracts (seaweed extract, alfalfa hydrolysate) and essential oils (thyme and juniper), and grey mold incidence on flowers was evaluated (Experiment 1). Furthermore, a second group of plants was treated before (Experiment 2) and after (Experiment 3) controlled inoculation with P. aphanis. The results indicated that the incidence of flower infected by B. cinerea was reduced by approximately 50% with thyme and juniper essential oils’ applications compared to the untreated control, with no significant difference observed compared to the commercial fungicide penconazole (positive control). As a consequence, the final yield of essential-oil-treated plants was +27% higher than that of non-treated plants. No significant differences emerged for other tested products against grey mold. However, gene expression analysis showed an up-regulation (>2 ÷ 5 folds as compared to control 4 days after application) of FaEDS1, FaLOX and PR gene expression (FaPR1, FaPR5, FaPR10) in leaves treated with BTH. The other natural substances tested also induced defense-related genes, albeit at a lower level than BTH. In Experiment 2, all treatments applied prior to inoculation significantly reduced the incidence and severity of powdery mildew as compared to control. At 28 days after inoculation, chitosan and thyme essential oil applications performed similarly to their positive controls (BTH and penconazole, respectively), showing a significant reduction in disease incidence (by −84 and −92%) as compared to control. Post-inoculum application of essential oils (Experiment 3) showed an efficacy similar to that of penconazole against powdery mildew. These results indicated that the tested substances could be used as alternatives to fungicides for the control of grey mold and powdery mildew in strawberry, therefore representing a valuable tool for the control of these fungal diseases under the framework of sustainable agriculture.

Real-Time Identification of Strawberry Pests and Diseases Using an Improved YOLOv8 Algorithm

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification.

Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8.

Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.

First report of Fusarium oxysporum f. sp. fragariae race 1 causing Fusarium wilt of strawberry (Fragaria × ananassa) in the Alentejo region of Portugal.

In the summer of 2023, within the Alentejo region (Portugal), a new occurrence of a plant disease of strawberry (Fragaria × ananassa) cv. 'Monterey' was observed in a Spring commercial planting. Symptoms consisted of foliar wilting, drying of older leaves, deformed and highly chlorotic leaflets, crown discoloration, plant stunting, and in some cases death. Several outbreak foci, covering nearly half a hectare, were observed within the affected farm, with almost 80% of the plants showing symptoms. Four samples,of 6 plants, were collected from 4 locations within the field. Petiole sections (1 cm) were rinsed with 0.1% Tween 20, submersed in 70% EtOH for 20 s followed by 60 s in 1% NaOCL, and then placed on Komada's medium (Komada, 1975). After incubation at room temperature in the dark for a week, white-colored fluffy mycelia grew profusely from the petioles of all samples. Colony morphology and non-septate, ellipsoidal microconidia (5.7-12.4×2.5-4.3 μm) borne on monophialides, exhibited a resemblance to Fusarium oxysporum (Leslie and Summerell, 2006). Ten single strains were obtained from different plants by single hyphal tip isolation. For molecular confirmation, a portion of the translation elongation factor 1-alpha (EF1α) was amplified by PCR using EF1/EF2 primers (O'Donnell et al., 1998). Additionally, the RNA polymerase subunit RPB2 was amplified as two contiguous fragments via primers and protocols described by O'Donnell et al., 2021. Amplicons were sequenced (GenBank Accession Nos. PP426617 - 426626, PQ058494 - 058513). Using Fusarium-ID and Fusarioid-ID databases, EF1α and RPB2 sequences were found to be more than 99% identical to published F. oxysporum type isolates and Fusarium sp. isolates in the F. oxysporum species complex (Crous et al., 2021; Wang et al., 2022). A specific F. oxysporum f. sp. fragariae (Fof) qPCR assay (Burkhardt et al., 2019) was used to determine if these isolates could be Fof race 1. A Fof race 1 isolate (MAFF305558), negative controls, and water controls were included. All ten isolates and the Fof race 1 control were positive (Ct < 30), while other Fusarium spp. used as negative controls and the water controls did not amplify. Two isolates (F.200 and F.202) and MAFF305558 (positive control) were included in a pathogenicity test on two strawberry cultivars, 'Monterey' (susceptible to race 1) and 'Fronteras' (resistant to race 1) (Dilla-Ermita et al., 2023). Each isolate was included in two independent trials. In each trial, 5 plants per cultivar were inoculated by dipping roots for 10 min in 5 × 106 conidia/mL of 0.1% water agar (WA) or in sterile 0.1% WA for the negative control plants. Each plant was then planted in a pot filled with peat. Pots were placed randomly in random positions in a growth chamber at 28/20°C and 12h photoperiod. After 8 - 10 weeks, the control plants and 'Fronteras' plants remained healthy, while the inoculated plants cv. 'Monterey' were severely wilted and/or dead. Fusarium oxysporum was re-isolated from all symptomatic plants. Recovered isolates were confirmed to be the same as the inoculated ones using the Fof-qPCR, including the same controls as above. This is the first report of Fof race 1 in the Iberian Peninsula. Given that the land was not previously used for strawberry, it is highly probable that the pathogen was introduced with the planting material originated from a Spanish nursery. In conclusion, it is imperative to implement more severe control measures in nurseries to avoid the spread of this race.

First report of Erwinia pyrifoliae causing flower blight and fruit rot on greenhouse-grown Fragaria × ananassa in the United States.

Erwinia pyrifoliae causes disease of pear (Pyrus spp.), apple (Malus spp.), and strawberry (Fragaria × ananassa) (Wenneker and Bergsma-Vlami 2015), which are economically important commodities in the US. Disease symptoms on pear and apple are indistinguishable from those caused by the non-quarantine fire blight pathogen, E. amylovora (Kim et al. 1999), which also causes disease on strawberries (Atanasova et al. 2005). Samples of greenhouse-grown strawberry 'Albion' from Ohio were submitted to the Purdue Plant and Pest Diagnostic Lab in December 2023. Fruits were stunted with brown lesions, while sepals and pedicels had brown-black water-soaked lesions. Cut fruit exuded bacterial ooze from the main vascular bundle. Bacterial streaming was observed microscopically from symptomatic tissue which tested positive with the E. amylovora ImmunoStrip® (Agdia Inc., Elkhart, IN); reported by the manufacturer to cross-react with E. pyrifoliae. Isolation from symptomatic tissue produced pure cultures on sucrose peptone agar and Kings medium B after incubation at 27°C for 48 hr, and colonies appeared circular and white/opaque. Crude DNA extractions were prepared by boiling colony suspensions in Tris-EDTA buffer. Two independent real-time PCR tests specific for E. pyrifoliae (Lehman et al. 2008; Yasuhara-Bell et al. 2024) produced positive results. Conventional PCR using an E. pyrifoliae-specific primer set targeting a divergent region between pstS and glmS genes (Wensing et al. 2011) also produced positive results. The amplicon was Sanger-sequenced and deposited into NCBI GenBank (Accession PP757383). BLASTn analysis using the Nucleotide collection and Whole-genome shotgun contigs revealed top matches (100% query coverage; 97.5% identity to type strain DSM 12163) with E. pyrifoliae only; next closest match was E. amylovora (53% query coverage). To confirm Koch's postulates, immature fruit of six healthy strawberry 'Albion' plants were wounded with a sterile pipette tip and then submersed in a bacterial suspension in sterile deionized water (DI H2O) (3.1×107 cells/ml). Fruit of six additional plants were mock inoculated using sterile DI H2O. Plants were placed in plastic bags for 48 hr at room temperature with a 12-hr photoperiod. Symptoms were first observed on inoculated plants 1.5 days post-inoculation (DPI). Brown discoloration was observed within fruit and as spreading lesions on fruit pedicels by 4 DPI; mock-inoculated plants remained asymptomatic. Bacterial streaming from symptomatic tissue allowed successful re-isolation of the bacterium. Molecular testing confirmed isolates to be E. pyrifoliae, thus completing Koch's postulates. Following initial confirmation, additional samples of infected strawberry ('Albion' and 'GB96') from the same greenhouse were confirmed positive for E. pyrifoliae by molecular testing and sequencing. To our knowledge this is the first time Erwinia pyrifoliae was detected in the US. There are many known pathways of introduction from Asia and Europe; however, pstS-glmS sequence comparison with strawberry isolates from the Netherlands (sequences provided by M.J.C. Pel) suggests this US strawberry strain is unique, but most closely related to Japanese strains (98.5% identity). Potential origin of this strain is unknown, but comparative genomics studies to investigate relatedness among strains are planned.

Antagonistic Strain Bacillus velezensis JZ Mediates the Biocontrol of Bacillus altitudinis m-1, a Cause of Leaf Spot Disease in Strawberry.

Biofertilizers are environmentally friendly compounds that can enhance plant growth and substitute for chemically synthesized products. In this research, a new strain of the bacterium Bacillus velezensis, designated JZ, was isolated from the roots of strawberry plants and exhibited potent antagonistic properties against Bacillus altitudinis m-1, a pathogen responsible for leaf spot disease in strawberry. The fermentation broth of JZ exerted an inhibition rate of 47.43% against this pathogen. Using an optimized acid precipitation method, crude extracts of lipopeptides from the JZ fermentation broth were obtained. The crude extract of B. velezensis JZ fermentation broth did not significantly disrupt the cell permeability of B. altitudinis m-1, whereas it notably reduced the Ca2+-ATPase activity on the cell membrane and markedly elevated the intracellular reactive oxygen species (ROS) concentration. To identify the active compounds within the crude extract, QTOF-MS/MS was employed, revealing four antimicrobial compounds: fengycin, iturin, surfactin, and a polyene antibiotic known as bacillaene. The strain JZ also produced various plant-growth-promoting substances, such as protease, IAA, and siderophore, which assists plants to survive under pathogen infection. These findings suggest that the JZ strain holds significant potential as a biological control agent against B. altitudinis, providing a promising avenue for the management of plant bacterial disease.

Pseudomonas fragariae sp. nov., a novel bacterial species causing leaf spots on strawberry (Fragaria×ananassa).

In Florida, angular leaf spot, caused by Xanthomonas fragariae, was the only known bacterial disease in strawberry, which is sporadic and affects the foliage and calyx. However, from the 2019-2020 to 2023-2024 Florida strawberry seasons, unusual bacterial-like symptoms were observed in commercial farms, with reports of up to 30 % disease incidence. Typical lesions were water-soaked and angular in early stages that later became necrotic with a circular-ellipsoidal purple halo, and consistently yielded colonies resembling Pseudomonas on culture media. Strains were pathogenic on strawberry, fluorescent, oxidase- and arginine-dihydrolase-negative, elicited a hypersensitive reaction on tobacco, and lacked pectolytic activity. Although phenotypic assays, such as fatty acid methyl profiles and Biolog protocols, placed the strains into the Pseudomonas group, there was a low similarity at the species level. Further analysis using 16S rRNA genes, housekeeping genes, and whole genome sequencing showed that the strains cluster into the Pseudomonas group but do not share more than 95 % average nucleotide identity compared to representative members. Therefore, the genomic and phenotypic analysis confirm that the strains causing bacterial spot in strawberry represent a new plant pathogenic bacterial species for which we propose the name Pseudomonas fragariae sp. nov. with 20-417T (17T=LMG 32456T=DSM 113340 T) as the type strain, in relation to Fragaria×ananassa, the plant species from which the pathogen was first isolated. Future work is needed to assess the epidemiology, cultivar susceptibility, chemical sensitivity, and disease management of this possible new emerging strawberry pathogen.

Diversity of Colletotrichum species on strawberry (Fragaria × ananassa) in Germany

Anthracnose caused by Colletotrichum species is an important disease of strawberries (Fragaria × ananassa), but the species causing this disease in Germany have not been investigated based on modern systematics. By using multi-locus phylogenetic analyses (ITS, act, gapdh, chs-1, his3, tub2), 58 Colletotrichum isolates from previous and recent collections, obtained mainly from fruit anthracnose of cultivated strawberries in Germany, were identified or re-identified as C. fioriniae, C. godetiae and C. nymphaeae (C. acutatum species complex) as well as C. anthrisci and C. lineola (C. dematium complex). Colletotrichum nymphaeae was dominant; most of the isolates belonged to one clonal lineage that occurs on strawberries throughout Europe, the United States of America, and some African and Asian countries. One of the other two haplotypes was distantly related and only represented by recently collected material. All other species, each of one haplotype, had only been isolated once or twice from German strawberries. This is the first report of C. anthrisci in Germany and for the genus Fragaria worldwide; all the other isolated fungi are newly reported for this genus in Germany. Comparisons of morphological characteristics of the species identified demonstrate that these features are of limited use for identification, even to species complex level. In pathogenicity tests, all five species caused anthracnose symptoms on ripe fruit of Fragaria × ananassa ‘Asia’.

Green synthesized lignin nanoparticles for the sustainable delivery of pyraclostrobin to control strawberry diseases caused by Botrytis cinerea

Lignin, renowned for its renewable, biocompatible, and environmentally benign characteristics, holds immense potential as a sustainable feedstock for agrochemical formulations. In this study, raw dealkaline lignin (DAL) underwent a purification process involving two sequential solvent extractions. Subsequently, an enzyme-responsive nanodelivery system (Pyr@DAL-NPs), was fabricated through the solvent self-assembly method, with pyraclostrobin (Pyr) loaded into lignin nanoparticles. The Pyr@DAL-NPs shown an average particle size of 250.4 nm, demonstrating a remarkable loading capacity of up to 54.70 % and an encapsulation efficiency of 86.15 %. Notably, in the presence of cellulase and pectinase at a concentration of 2 mg/mL, the release of Pyr from the Pyr@DAL-NPs reached 92.66 % within 120 h. Furthermore, the photostability of Pyr@DAL-NPs was significantly improved, revealing a 2.92-fold enhancement compared to the commercially available fungicide suspension (Pyr SC). Bioassay results exhibited that the Pyr@DAL-NPs revealed superior fungicidal activity against Botrytis cinerea over Pyr SC, with an EC50 value of 0.951 mg/L. Additionally, biosafety assessments indicated that the Pyr@DAL-NPs effectively declined the acute toxicity of Pyr towards zebrafish and posed no negative effects on the healthy growth of strawberry plants. In conclusion, this study presents a viable and promising strategy for developing environmentally friendly controlled-release systems for pesticides, offering the unique properties of lignin.

First Report ofCoptis Wilt Disease Caused by Plectosphaerella cucumerina in China.

Coptis (Coptis chinensis) belongs to the Ranunculaceae family, the rhizomes used in traditional Chinese medicine. Since 2021, an uncommon stem and leaf wilt disease, with an average disease incidence of 70%~90%, has been observed in Guangdong and Guangxi provinces. The early wilt symptoms were observed on older leaves and stems, and the whole seedling wilted and died. The rhizome of the diseased seedlings changed in color, became necrotic, and rotted. Symptomatic roots and stems were surface-sterilized with 70% ethanol for 30 s, followed by 0.2% NaClO for 2-3 min, rinsed in sterile water three times, and then placed on potato dextrose agar (PDA) at 25℃for 14 days. Fungal growth was observed, and six isolates with similar morphology were obtained. The 14-day-old colonies on PDA were buff with few aerial hyphae and slimy surfaces. Aerial hyphae were sparse with simple or branched conidiophores. Conidia were hyaline, smooth, ovoid, septate or aseptate, and 5.77 to 9.53 × 2.15 to 3.32 µm (n = 50). Three of the six isolates were subjected to further analysis. The genomic DNA of three isolates (CCF1-1, CCF1-2, CCF1-3) was extracted using Axygen MAG-FRAG- I-50 (Axygen Bio-Tek) for molecular identification. Partial sequences of the internal transcribed spacer of rDNA (ITS) and large subunit rDNA (LSU) were amplified using the primers ITS1/4 and LR5F/LROR, respectively (Vilgalys and Hester 1990). Their sequences were aligned by MEGA X (Kumar et al., 2018), and the sequences of each region showed 100% sequence similarity among our isolates. A BLAST search of ITS and LSU sequences (accession nos. ON377369, ON428244) showed that both regions had the highest nucleotide similarities (99.43 to 99.89%) to the Plectosphaerella cucumerina strains. Based on morphological and molecular analyses, the isolates were identified as P. cucumerina (Palm et al. 1995). The pathogenicity of our isolates CCF1-1, CCF1-2, CCF1-3 was tested on ten 2-month-old healthy seedlings of coptis, respectively. For the seedlings, 30 ml of fungal conidial suspension (1×106 conidia/ml) or sterile water, as control, were poured into their root area. Conidia suspension were prepared from 14-day-old cultures on PDA by eluting with sterilized water. The seedlings were incubated at 25°C and 75% relative humidity under a 12-h/12-h light/dark cycle. The test was repeated three times. After 20 days, only seedlings inoculated with P. cucumerina exhibited symptoms similar to those diseased seedlings in the field. The control seedling had no symptoms. The morphologically similar fungus was re-isolated from the tested seedlings, thus fulfilling Koch's postulates. Based on molecular, morphological, and pathogenic properties, P. cucumerina is the causal fungal pathogen of coptis wilt disease. Previously, P. cucumerina has been related to wilt disease in strawberry and Chinese cabbage (Yang et al. 2023; Gao et al. 2022), but to our knowledge, this is the first report of P. cucumerina causing wilt disease on coptis in China. Coptis wilt disease tends to occur in a warm and rainy environment, and strengthening the detection and quarantine of seedlings is the key to preventing the occurrence and spread of the disease.

Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks.

Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first five and last five layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.

A Semi-Selective Medium to Evaluate Over-Summering Survival of Neopestalotiopsis sp. in Florida Strawberry Fields.

Devastating disease outbreaks with leaf spot symptoms and fruit rot caused by a fungus identified as a cryptic species of Neopestalotiopsis have generated concern in the Florida strawberry industry. Some Neopestalotiopsis species are saprobes in soil and plant debris, but the ability of the new Neopestalotiopsis sp. to survive in strawberry debris was unknown. Therefore, the objectives of this study were to develop a semiselective medium for isolation of Neopestalotiopsis spp. and to evaluate Neopestalotiopsis sp. survival in soil and strawberry plant debris over the summer in Florida. The Neopestalotiopsis semiselective medium (NSM) inhibited growth of most fungal species pathogenic to strawberry in Florida, except Neopestalotiopsis rosae, Neopestalotiopsis sp., and Phomopsis obscurans, in addition to Penicillium spp., which are nonpathogenic. However, Neopestalotiopsis species, P. obscurans, and Penicillium spp. could be distinguished in the medium through morphological characteristics. Soil samples arbitrarily collected from six commercial fields toward the end of the season (May) and before (July) and after (September) preplant soil fumigation in the following season were processed with NSM, and Neopestalotiopsis spp. populations were calculated as CFU per gram of soil. CFU ranged from 48.3 to 2,410.8 at the end of the season and from 1.7 to 630.8 before soil fumigation, but Neopestalotiopsis spp. were not recovered after soil treatment. However, 1.7 to 25 CFU were obtained from the nontreated areas in the row middles. Neopestalotiopsis sp. survival was also evaluated on diseased strawberry plants (leaves and crowns) exposed to the environment for 17 months. On leaves, Neopestalotiopsis spp. were recovered for 6 months before leaf decomposition, whereas the number of CFU on crowns declined monthly, but colonies were still recovered during the final evaluation months. A high-resolution melting analysis confirmed most of the colonies from soil and crowns were the new Neopestalotiopsis sp. Our results show Neopestalotiopsis sp. can survive in soil and strawberry debris under Florida summer conditions and may serve as a source of inoculum for the subsequent season.

Biological Control of Root Rot of Strawberry by Bacillus amyloliquefaciens Strains CMS5 and CMR12.

Strawberry root rot caused by Fusarium solani is one of the main diseases of strawberries and significantly impacts the yield and quality of strawberry fruit. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional chemical fungicides. To obtain antagonistic bacteria with a high biocontrol effect on strawberry root rot, over 72 rhizosphere bacteria were isolated from the strawberry rhizosphere soil and screened for their antifungal activity against F. solani by dual culture assay. Among them, strains CMS5 and CMR12 showed the strongest inhibitory activity against F. solani (inhibition rate 57.78% and 65.93%, respectively) and exhibited broad-spectrum antifungal activity. According to the phylogenetic tree based on 16S rDNA and gyrB genes, CMS5 and CMR12 were identified as Bacillus amyloliquefaciens. Lipopeptide genes involved in surfactin, iturin, and fengycin biosynthesis were detected in the DNA genomes of CMS5 and CMR12 by PCR amplification. The genes related to the three major lipopeptide metabolites existed in the DNA genome of strains CMS5 and CMR12, and the lipopeptides could inhibit the mycelial growth of F. solani and resulted in distorted hyphae. The inhibitory rates of lipopeptides of CMS5 and CMR12 on the spore germination of F. solani were 61.00% and 42.67%, respectively. The plant-growth-promoting (PGP) traits in vitro screening showed that CMS5 and CMR12 have the ability to fix nitrogen and secreted indoleacetic acid (IAA). In the potting test, the control efficiency of CMS5, CMR12 and CMS5+CMR12 against strawberry root rot were 65.3%, 67.94% and 88.00%, respectively. Furthermore, CMS5 and CMR12 enhanced the resistance of strawberry to F. solani by increasing the activities of defense enzymes MDA, CAT and SOD. Moreover, CMS5 and CMR12 significantly promoted the growth of strawberry seedlings such as root length, seedling length and seedling fresh weight. This study revealed that B. amyloliquefaciens CMS5 and CMR12 have high potential to be used as biocontrol agents to control strawberry root rot.

Plant pathology identification using local-global feature level based on transformer

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.

Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging

Botrytis cinerea is a strawberry disease that causes economic loss worldwide. If a disease outbreak occurs during storage or transportation, it can spread rapidly to neighboring objects; thus, there is a need to develop early diagnostic techniques to prevent it. In this study, we developed a method to rapidly and nondestructively determine the infection stage in strawberry fruit using hyperspectral fluorescence imaging. ‘Keumsil’ cultivar strawberries were used, and hyperspectral fluorescence images were acquired over 144 h in control and inoculation groups. Strawberries were categorized into four infection stages based on visible mold spores: healthy, asymptomatic, infected, and after-infected. Hyperspectral fluorescence spectra were extracted to develop a one-dimensional convolutional neural network (1D-CNN) model based on partial least squares-discriminant analysis (PLS-DA), VGG-19, and ResNet-50; data augmentation techniques and six spectral preprocessing techniques were applied to the datasets. The application of data augmentation techniques improved the performances of the PLS-DA and 1D-CNN models in determining the infection stage. The performance of the ResNet-50-based 1D-CNN model with mean normalization data and data augmentation technique was the best, with 96.88% precision, 96.87% recall, 96.85% F1-score, and 96.86% accuracy. The results of this study showed that it is possible to determine the infection stage of Botrytis cinerea on strawberry fruit using hyperspectral fluorescence imaging and 1D-CNN techniques. This technology is expected to be applied for the early detection of Botrytis cinerea in strawberry growth, postharvest sorting and packing, and distribution stages.

Impact of Soil-Applied Thyme Oil on Strawberry Yield and Disease Abundance

ABSTRACT Strawberry growers across the United States are looking for sustainable alternatives to conventional pesticides for management of soil-borne or crown-rot causing pathogens, such as plant-extracts like thyme oil. The objectives of this study were to determine the impact of soil-applied thyme oil fungicides on strawberry plant health, yield, sweetness, and foliar disease, and on the assemblage of soil microbial communities in strawberry plasticulture systems. In 2021 and 2022 the interaction of three soil-applied fungicides and three foliar fungicide programs were explored. Thyme oil was not found to improve strawberry plant growth or yield when compared to strawberries in the control. However, plants treated with mefenoxam exhibited an average winter crown weight of 2.11 g, significantly lower than control and thyme oil treatments at 3.14 g and 3.16 g, respectively. This translated into a significant yield loss where mefenoxam treated plants exhibited lowered yields at 416.04 g of fruit per plant compared to 523.57 g and 521.53 g per plant in the control and thyme oil treatments, respectively. A high incidence of foliar and fruit diseases was present; however, no interaction of soil and foliar fungicide treatments were observed for yield or fruit sweetness. Minimal root protection from pathogens was observed for all treatments and Fusarium, Pythium and Phytophthora fungi were regularly observed. Mefenoxam was observed to increase the recovery of oomycetes, which may cause Pythium and Phytophthora. No benefit from thyme oil was observed at our site and its addition as a soil fungicide would likely not benefit most growers.

BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification

With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F1-score, and parameters. It holds significant promise for applications in strawberry disease identification.

Efficacy of Using Chemical Inducers and Biological Agents to Control Strawberry Leaf Spot Disease on Chemical Components and Enzyme Activity

Strawberry plants are subjected to many pathogens. Fungal diseases of strawberries are important worldwide and occur in all parts of the plant, including leaves, crowns, and fruits. The results of the bio-agent experiment indicated that Trichoderma harzianum was the most effective in reducing the growth of Alternaria alternata and Botrytis cinerea in vitro, the main causes of leaf spots in strawberry plants. The inhibition zone was observed and mycelium of T. harzianum invaded the colony of the tested pathogens. The importance of the biochemical study of defense reaction in the physiology of disease resistance was accepted. The activity of total, free, and conjugated phenols as well as peroxidase, polyphenol oxidase, and catalase enzyme activities were determined in resistant and susceptible strawberry cultivars. All the total, free, and conjugated phenols increased in resistant cultivars (Fortuna and Winter Star) and decreased in susceptible cultivar (Festival). Oxidative enzymes, like the increased activity of enzymes that appear mince of new polypeptide protein, have become models in the study of plant disease resistance. The higher content of peroxidase, polyphenol oxidase, and catalase enzymes in resistant cultivars, were noticed compared with those in susceptible ones. It was found also that, the chemical inducers increased total phenols in resistant and susceptible cultivars i.e., potassium di-phosphate, ferrous sulphate, and oxalic acid. Inducer resistance also caused an increase of free phenols than the control. However, conjugated phenols accumulated faster after using chemical inducers in the leaves of the resistant cultivars, than the susceptible ones.