Cucumber Leaf Research Articles

Fruit and vegetable leaf disease recognition based on a novel custom convolutional neural network and shallow classifier.

Fruits and vegetables are among the most nutrient-dense cash crops worldwide. Diagnosing diseases in fruits and vegetables is a key challenge in maintaining agricultural products. Due to the similarity in disease colour, texture, and shape, it is difficult to recognize manually. Also, this process is time-consuming and requires an expert person. We proposed a novel deep learning and optimization framework for apple and cucumber leaf disease classification to consider the above challenges. In the proposed framework, a hybrid contrast enhancement technique is proposed based on the Bi-LSTM and Haze reduction to highlight the diseased part in the image. After that, two custom models named Bottleneck Residual with Self-Attention (BRwSA) and Inverted Bottleneck Residual with Self-Attention (IBRwSA) are proposed and trained on the selected datasets. After the training, testing images are employed, and deep features are extracted from the self-attention layer. Deep extracted features are fused using a concatenation approach that is further optimized in the next step using an improved human learning optimization algorithm. The purpose of this algorithm was to improve the classification accuracy and reduce the testing time. The selected features are finally classified using a shallow wide neural network (SWNN) classifier. In addition to that, both trained models are interpreted using an explainable AI technique such as LIME. Based on this approach, it is easy to interpret the inside strength of both models for apple and cucumber leaf disease classification and identification. A detailed experimental process was conducted on both datasets, Apple and Cucumber. On both datasets, the proposed framework obtained an accuracy of 94.8% and 94.9%, respectively. A comparison was also conducted using a few state-of-the-art techniques, and the proposed framework showed improved performance.

Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach

It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases.

Humic acid and grafting as sustainable agronomic practices for increased growth and secondary metabolism in cucumber subjected to salt stress

Salinity stress poses a significant treat to crop yields and product quality worldwide. Application of a humic acid bio stimulant and grafting onto tolerant rootstocks can both be considered sustainable agronomic practices that can effectively ameliorate the negative effects of salinity stress. This study aimed to assess the above mentioned ameliorative effects of both practices on cucumber plants subjected to saline environments. To attain this goal a factorial experiment was carried out in the form of a completely randomized design with three replications. The three factors considered were (a) three different salinity levels (0, 5, and 10 dS m−1 of NaCl), (b) foliar application of humic acid at three levels (0, 100, and 200 mg L−1), and (c) both grafted and ungrafted plants. Vegetative traits including plant height, fresh and dry weight and number of leaf exhibited a significant decrease under increasing salinity stress. However, the application of humic acid at both levels mitigated these effects compared to control plants. The reduction in relative water content (RWC) of the leaf caused by salinity, was compensated by the application of humic acid and grafting. Thus, the highest RWC (86.65%) was observed in grafting plants with 0 dS m−1 of NaCl and 20 mg L−1 of humic acid. Electrolyte leakage (EL) increased under salinity stress, but the application of humic acid and grafting improved this trait and the lowest amount of EL (26.95%) was in grafting plants with 0 dS m−1 of NaCl and 20 mg L−1 of humic acid. The highest amount of catalase (0.53 mmol H2O2 g−1 fw min−1) and peroxidase (12.290 mmol H2O2 g−1 fw min−1) enzymes were observed in the treatment of 10 dS m−1 of NaCl and 200 mg L−1 humic acid. The highest amount of total phenol (1.99 mg g−1 FW), total flavonoid (0.486 mg g−1 FW), total soluble carbohydrate (30.80 mg g−1 FW), soluble protein (34.56 mg g−1 FW), proline (3.86 µg g−1 FW) was in grafting plants with 0 dS m−1 of NaCl and 200 mg L−1 of humic acid. Phenolic acids and phenylalanine ammonia lyase (PAL) and polyphenol oxidase (PPO) enzymes increased with increasing salinity and humic acid levels. Contrary to humic acid, salt stress increased the sodium (Na+) and chlorine (Cl−) and decreased the amount of potassium (K+) and calcium (Ca2+) in the root and leaf of ungrafted cucumber. However, the application 200 mg L−1 humic acid appeared to mitigate these effects, thereby suggesting a potential role in moderating physiological processes and improving growth of cucumber plants subjected to salinity stress. According to the obtained results, spraying of humic acid (200 mg L−1) and the use of salt resistant rootstocks are recommended to increase tolerance to salt stress in cucumber. These results, for the first time, clearly demonstrated that fig leaf gourd a new highly salt-tolerant rootstock, enhances salt tolerance and improves yield and quality of grafted cucumber plants by reducing sodium transport to the shoot and increasing the amount of compatible osmolytes.

Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.

Distribution and survival of the predatory mite Blattisocius mali on cucumber leaves with the addition of bran, yeast and pollen

Biological control has a special position in sustainable agriculture that requires continuous exploration and diversification in bio-agents to cope with emerging crop pests. Blattisocius mali is a promising biological control agent against some acarid mites, nematodes and moth pests. This study aimed to examine factors that could increase survival and diminish dispersal of B. mali deprived of its prey, the mold mite Tyrophagus putrescentiae, from cucumber plants. The impact of the presence of males on starving females’ lifespans and the influence of different substrates, i.e., wheat bran, dry yeast pellets, and cattail pollen, on the distribution and survival of starving females in groups with males were examined. Experiments were performed on cucumber leaf platforms in Petri dishes filled with water. The results showed that females lived longer and a lower percentage drowned in water when accompanied by males. On the platforms with the addition of a substrate, the mites mainly stayed within the substrate, and eggs were only recorded in the substrate. They clearly preferred bran over pollen or pollen + yeast pellet aggregations. However, the quality of the substrate deteriorated within the first days, and some mites died of entrapment in the substrates. On the 3rd day, the lowest mean percentage of live individuals was observed on platforms with yeast + pollen (54.4%) followed by pollen (68.9%) alone. At that time, females also stopped laying eggs, and cannibalism towards hatched larvae and adults was observed. By the end of the experiment, 54.67% of the mites had been found dead on the leaf surface, and none of the substrates had significantly influenced their lifespan. The females lived on average 8.19 days and the males 5.06 days. The obtained results are discussed in the context of potential application of B. mali in biological control strategies.

A mutation in CsGME encoding GDP-mannose 3,5-epimerase results in little and wrinkled leaf in cucumber.

Map-based cloning revealed that a mutation in a highly conserved amino acid of the CsGME gene encoding GDP-mannose 3,5-epimerase, causes the phenotype of little and wrinkled leaves in cucumbers. Leaf size is a critical determinant of plant architecture in cucumbers, yet only a few genes associated with this trait have been mapped or cloned. Here, we identified and characterized a mutant with little and wrinkled leaves, named lwl-1. Genetic analysis revealed that the phenotype of the lwl-1 was controlled by a single recessive gene. Through map-based cloning, the lwl-1 locus was narrowed down to a 12.22-kb region exclusively containing one fully annotated gene CsGME (CsaV3_2G004170). CsGME encodes GDP-mannose 3,5-epimerase, which is involved in the synthesis of ascorbic acid (ASA) and one of the components of pectin, RG-II. Whole-length sequencing of the 12.22kb DNA fragment revealed the presence of only a non-synonymous mutation located in the sixth exon of CsGME in lwl-1, resulting in an amino acid alteration from Pro363 to Leu363. This mutation was unique among 118 inbred lines from cucumber natural populations. CsGME expression significantly reduced in various organs of lwl-1, accompanied by a significant decrease in ASA and pectin content in leaves. Both CsGME and Csgme proteins were localized to the cytoplasm. The mutant phenotype exhibited partial recovery after the application of exogenous boric acid. Silencing CsGME in cucumber through VIGS confirmed its role as the causal gene for lwl-1. Transcriptome profiling revealed that CsGME greatly affected the expression of genes related to the cell division process and cell plate formation. This study represents the first report to characterize and clone the CsGME in cucumber, indicating its crucial role in regulating leaf size and development.

CsRAXs negatively regulate leaf size and fruiting ability through auxin glycosylation in cucumber.

Leaves are the main photosynthesis organ that directly determines crop yield and biomass. Dissecting the regulatory mechanism of leaf development is crucial for food security and ecosystem turn-over. Here, we identified the novel function of R2R3-MYB transcription factors CsRAXs in regulating cucumber leaf size and fruiting ability. Csrax5 single mutant exhibited enlarged leaf size and stem diameter, and Csrax1/2/5 triple mutant displayed further enlargement phenotype. Overexpression of CsRAX1 or CsRAX5 gave rise to smaller leaf and thinner stem. The fruiting ability of Csrax1/2/5 plants was significantly enhanced, while that of CsRAX5 overexpression lines was greatly weakened. Similarly, cell number and free auxin level were elevated in mutant plants while decreased in overexpression lines. Biochemical data indicated that CsRAX1/5 directly promoted the expression of auxin glucosyltransferase gene CsUGT74E2. Therefore, our data suggested that CsRAXs function as repressors for leaf size development by promoting auxin glycosylation to decrease free auxin level and cell division in cucumber. Our findings provide new gene targets for cucumber breeding with increased leaf size and crop yield.

A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet

Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face challenges in accurately estimating disease severity under complex backgrounds and achieving real-time performance.This paper presents a lightweight severity estimation method called DM-BiSeNet to address these challenges. The proposed method utilizes BiSeNet V2 as the base network and incorporates depthwise separable convolutional blocks to optimize the detail branch. A simplified MobileNet V3 network is also constructed to optimize the semantic branch. The model training process is accelerated using the AdamW optimizer. To evaluate the performance of DM-BiSeNet, a dataset consisting of cucumber powdery mildew and downy mildew disease images collected in natural scenes is utilized. Experimental results demonstrate that DM-BiSeNet achieves higher accuracy in severity estimation, with an R2 value of 0.9407 and an RMSE of 1.0680, outperforming the comparison methods. Moreover, DM-BiSeNet exhibits a complexity of 1.54 GFLOPs and is capable of reasoning 94 disease images per second.The proposed DM-BiSeNet model offers a lightweight and effective solution for accurate and rapid severity estimation of cucumber diseases under complex backgrounds. It provides a valuable technical tool for quantitative disease estimation, offering significant potential for practical applications.

Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.

Molecularly Imprinted Polymer‐Based Electrochemical Sensor for In Situ Detection of Free Proline in Cucumber Leaves

AbstractProline is an important amino acid, which is crucial to plant growth and development. Accurate analysis of proline content is of great significance for understanding its physiological mechanism in plants. In this paper, according to the requirements for in‐situ detection in plants in precision agriculture, an electrochemical molecular imprinted polymers (MIP) sensor for determining proline in living plants was developed. Polypyrrole (PPy) was used as the functional monomer. To improve the performance of the MIP sensor, Au nanoparticles (NPs) were electrodeposited on the screen‐printed electrodes (SPEs) electrode. Thionine (Thi) was then electropolymerized on the SPEs to be used as an internal reference signal molecule. The MIP‐based proline sensor has the widest detection range of 1×10−16 −0.01 M. And its detection limit is the lowest (9.18 aM) so far. It was also used for measuring free proline in the leaves of living cucumber seedlings under salt stress. The MIP‐based proline sensor has an important prospect for detecting the physiological status of plants in situ and will play an important role in smart agriculture.

A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net

Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose.

Catalytic and antimicrobial activities of cucumber leaf extract-based organic@inorganic hybrid nanoflowers through Fenton-like reaction

Catalytic and antimicrobial activities of cucumber leaf extract-based organic@inorganic hybrid nanoflowers through Fenton-like reaction

Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning

Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning

Target of rapamycin (TOR) plays a role in regulating ROS-induced chloroplast damage during cucumber (Cucumis sativus) leaf senescence.

In cucumber production, delaying leaf senescence is crucial for improving cucumber yield and quality. Target of rapamycin (TOR) is a highly conserved serine/threonine protein kinase in eukaryotes, which can integrate exogenous and endogenous signals (such as cell energy state levels) to stimulate cell growth, proliferation, and differentiation. However, no studies have yet examined the regulatory role of TOR signalling in cucumber leaf senescence. In this study, the effects of TOR signalling on dark-induced cucumber leaf senescence were investigated using the TOR activator MHY1485 and inhibitor AZD8055 combined with transient transformation techniques. The results indicate that TOR responds to dark-induced leaf senescence, and alterations in TOR activity/expression influence cucumber leaf resistance to dark-induced senescence. Specifically, in plants with elevated TOR activity/expression, we observed reduced expression of senescence-related genes, less membrane lipid damage, decreased cell apoptosis, lower levels of reactive oxygen species production, and less damage to the photosynthetic system compared to the control. In contrast, in plants with reduced TOR activity/expression, we observed higher expression of senescence-related genes, increased membrane lipid damage, enhanced cell apoptosis, elevated levels of reactive oxygen species production, and more damage to the photosynthetic system. These comprehensive results underscore the critical role of TOR in regulating dark-induced cucumber leaf senescence. These findings provide a foundation for controlling premature leaf senescence in cucumber production and offer insights for further exploration of leaf senescence mechanisms and the development of more effective control methods.

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Formanilides from the culture broth of Perenniporia fraxinea.

A new formanilide dimer, fraxinin (1), and three known formanilides (2‒4) were isolated from the culture broth of Perenniporia fraxinea using silica gel and Sephadex LH-20 column chromatographies, medium-pressure liquid chromatography (MPLC), and preparative HPLC. The structures of these compounds were determined by spectroscopic methods, such as NMR and mass analysis, and by comparison of the spectra with previously reported data. The free radical scavenging activities of the isolated compounds were assessed using 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radicals. Compounds 1‒3 exhibited ABTS radical scavenging activity with IC50 values in the range of 57.2-250.2 μM. Compounds 2 and 4 marginally reduced disease incidence of powdery mildew with a control value of 42% at 1.0 mg ml-1 in cucumber leaf disk assay.

Environmental effects on the sporulation of Corynespora cassiicola in cucumber leaf spots

Environmental effects on the sporulation of <i>Corynespora cassiicola</i> in cucumber leaf spots

Suspect screening of pesticide co-formulants in fruits, vegetables and leaves by liquid and gas chromatography coupled to high resolution mass accuracy spectrometry: Potential impact on human health

Vegetables can contain co-formulants derived from the use of plant protection products (PPPs) in crops. Thus, in the current study co-formulants were determined in different fruits and vegetables and their leaves by gas and liquid chromatography coupled to Q-Orbitrap high-resolution mass spectrometry (GC-Q-Orbitrap and LC-Q-Orbitrap-MS). A total of 37 co-formulants were tentatively identified, and among them, 12 compounds were quantified by LC-Q-Orbitrap-MS and 9 by GC-Q-Orbitrap-MS. The mean co-formulant levels in fruit and vegetable samples was 92% lower than in leaf samples. Selected samples showed a high concentration of 1-ethyl-2-pyrrolidone among the co-formulants detected. This compound ranged from 22 µg/kg (strawberry) to 722 µg/kg (red grape), whereas in the case of leaves, its concentration was up to 6513 µg/kg in cucumber leaf. In addition, it has an LD50 equal to 1.440 g/kg. Therefore, this type of PPP co-formulants should be controlled in fruits and vegetables to avoid adverse health effects.

Leaf spot on cucumber caused by Stemphylium vesicarium newly reported in China

Leaf spot is one of the common diseases of Cucurbitaceae crops, including cucumber, muskmelon, and pumpkin. Between August and October 2021, cucumber leaves exhibiting disease symptoms were observed in farmland around Qingdao city, Shandong province. The corresponding diseased leaves were collected to isolate and characterize the causal agent of cucumber leaf spot. Multilocus phylogenetic analysis (internal transcribed spacer regions 1 and 2 and intervening 5.8S nrDNA, calmodulin, and glyceraldehyde-3-phosphate dehydrogenase) and morphological characteristics revealed that the fungal isolates belonged to Stemphylium vesicarium. The symptoms of brown to black lesions appeared on all artificially inoculated cucumber leaves, which were highly similar to those observed in the field. The pathogen was re-isolated from the corresponding diseased leaves to fulfill Koch's postulates. Therefore, S. vesicarium was confirmed as the pathogen of cucumber leaf spot in this study. To our knowledge, this is the first report of S. vesicarium causing leaf spot on cucumber in China.

Flooding-based MobileNet to identify cucumber diseases from leaf images in natural scenes

Cucumber production in China is declining due to various pathologic diseases, but the technology for plant disease detection is not mature and requires high labor costs. Moreover, since planting sites are typically high-density scenes, most photos are shot from various angles with messy backgrounds, resulting in poor classification reliability. In this paper, batches of cucumber leaf image data are collected from agricultural websites and then preprocessed through the image size normalization. A mobile-based recognition algorithm is proposed to identify cucumber diseases from leaf images in natural scenes, enabling farmers to detect diseases more quickly. The proposed algorithm allows farmers to upload cucumber pictures, and rapidly and accurately classify them with high accuracy. With a improved network based on MobileNet V3, the classification of seven kinds of cucumber leaf diseases can be quickly and accurately completed. The network model is achieved by selecting appropriate parameters, optimizers, and batch capacity using the single-variable method. Additionally, a new training strategy called the flooding method is applied in the model, replacing the traditional strategy that relies solely on loss decline. An accuracy of 83.3% is achieved on our custom dataset. Finally, two public datasets, namely PlantVillage and Apple Disease, are selected for migration experiments. The achieved accuracy rates for these datasets are 99% and 98.1% respectively, demonstrating the universality of the proposed strategy. The code for all the experiments will be made available for reference on the GitHub repository at https://github.com/YiQuanMarx/Agricultural_Diseases_Dentification.