Home An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
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An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology

  • Sharaf Alzoubi EMAIL logo , Malik Jawarneh , Qusay Bsoul , Ismail Keshta , Mukesh Soni and Muhammad Attique Khan
Published/Copyright: November 24, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.

Keywords: artificial intelligence; classification; fig leaf disease; image processing; machine learning techniques.

1 Introduction

Image processing is increasingly popular in the field of agriculture [1,2]. In fact, as a domain of technology, image processing is experiencing a rapid advancement, with the application of many tools to capture images, like cameras and satellites. Via a computer, the captured images are processed using various analysis techniques, to produce the desired information. Like in other sectors, image processing has eased the agriculture sector in resolving many issues, especially in classifying and detecting diseases inflicting crops. Through image processing, the sickly plant parts like leaf can be identified, and the inflicted area can be measured and diagnosed.

Image processing includes the use of many techniques to improve an image to allow the extraction of information from the image [3,4]. From a single image, several images can be generated. Additionally, some images need to be altered or enhanced to make them usable in other context, and images can be enhanced and altered via image processing. In image enhancement through image processing, several aspects of the image can be altered, for instance, the image noise, color, and sharpness. Through image processing also, images can be segmented and their features can be extracted.

Images come in various sizes, some are large while some are small. For large images, they need to be segmented to ease the next process of feature extraction. In image segmentation, the image, especially the digital image, is split into various smaller images [5]. Texture-based methods, thresholding methods, and color-based methods are among the commonly used methods in image segmentation. The step following image segmentation is the feature extraction step, whereby the dimensionality of the image would be minimized, so that the image will be left with just its most important and discernible aspects. Concurrently, large pictures could be rapidly matched, while feature representations are being reduced, with the application of this method. During image categorization, each picture is placed in specific category based on certain fixed criteria.

To fulfill our study’s overarching purpose, we have outlined several specific objectives. First, we aim to harness advanced image processing techniques to achieve accurate and robust identification and classification of fig leaf diseases. Second, through image enhancement methods, we seek to optimize the quality and usability of captured agricultural images. Third, we intend to explore image segmentation techniques, with a particular emphasis on handling large agricultural images effectively by partitioning them into manageable segments. Additionally, our study focuses on feature extraction from images, with the primary goal of reducing dimensionality while enhancing feature representations to facilitate disease classification. Finally, we aim to establish clear and effective criteria for disease categorization through comprehensive image analysis, enabling precise classification.

The agriculture industry has to be properly managed because crops affect the well-beings of mankind. Hence, diseases of crops need to be promptly and correctly detected, diagnosed, and classified [6], as part or regular monitoring of plant health. Plant diseases are detected and classified using certain detection and classification methods. There are several available methods for the purpose; some could identify only specific disease and symptoms While others have the capability to identify specific diseases and symptoms from a broad spectrum of possibilities. The use of image processing in plant disease identification is initiated by an input comprising a digital color image of an infected plant parts (e.g., leaf, fruit, or stem) to a disease identification system run by a computer. The image needs to have clear background to ease the disease identification because the presence of irrelevant elements or objects will reduce the accurateness of the results. It is also necessary to control the image’s capture settings to facilitate disease identification [7,8].

In the subsequent sections of this article, we will delve into a detailed exposition of our methodology, present our findings, and discuss their implications for the agricultural sector. By addressing these objectives, our study contributes to advancing the field of agricultural image processing and holds promise for improving agricultural productivity and crop disease management. The approach includes support vector machines (SVMs) [9] and image processing, with steps displayed in Figure 1.

Figure 1 (a–d) Fig leaf disease [10].
Figure 1

(a–d) Fig leaf disease [10].

2 Literature survey

Agricultural image processing helps in resolving issues related to agriculture, especially in identifying and classifying plant diseases. In fact, the detection and classification of plant diseases can increase the well-being of the agricultural industry, as it simplifies plant health monitoring and aids in the management of plant diseases. To this end, a number of studies have been carried out to explore plant disease identification and classification, on several common and important crops. Some of these studies are discussed in this section.

Rice leaf disease detection was demonstrated by Sanyal and Patel [11] involving 400 rice leaf images. Diseases of rice leaf can be caused by several factors including mineral insufficiencies. Brown spots called lesions of different shapes and sizes would appear on the inflicted leaves. In this study, the author employed ANN with single hidden layer, namely the Multilayer Perceptron (MLP) to detect diseased rice leaves. The RGB images were transformed into HSI color space, and then the colors and textures of the leaf images were fed to the proposed ANN. Utilizing entropy-based thresholding, the author segmented the images. Next the segmented images were transformed into a gray scale image after being analyzed using an edge detection technique. Classification of the disease was performed using self-organizing maps.

Meunkaewjinda et al. [12] proposed the use of MLP-ANN and SVM in a system, in the identification of grape leaf disease. In this intelligent system, MLP-ANN was used in detecting image object, namely the grape leaves, and the image background. SVM was used for identifying the diseased portions of the leaf while multiclass SVM was used to class the disease. In their study to determine the nitrogen level in barley leaves, Pagola et al. [13] employed RGB alterations, Principal component analysis (PCA), and softmax regression. The authors compared the accuracy of the three methods against the results from the use of chlorophyll meter. The authors concluded from their study that the barley leaf in the images did not have adequate nitrogen level. Carmargo and Smith [14] in their study of diseases of cotton plant, employed picture pattern classification algorithm in their disease diagnosis. An on-one approach SVM was used in classifying the segmented cotton plant images, focusing on the texture characteristics. The proposed method was successful.

In their study, Jian and Wei [15] used SVM-based technique to detect diseases on images of cucumber leaves. Features on the image were extracted utilizing basic thresholding method. The features were used in SVM training. The performance of the model was compared by using radial basis function kernel, polynomial kernel, and sigmoid kernel function on SVM, and the results showed the best effectiveness of radial basis function kernel. Nutrient deficiency on palm plants can be detected using a spectrometer. First, palm plant images were segmented according to color similarities, and then, an algorithm was used to extract the color and texture features. Next fuzzy classifiers were used on the obtained features to class the obtained data.

A classifier was employed to examine undernourished tomato leaves. In the process of color and texture feature extraction, the L*  a*  b* and RGB color spaces transformed into one another utilizing Fourier transforms, wavelet packets, and percent intensity histograms. Fuzzy K-nearest neighbor model was applied in the classification of the extracted features. In general, the achieved accuracy level was 82.5%. Wang et al. [16] employed neural networks to classify diseases on wheat and grapevine from captured images of wheat and grapevine. The authors used K-means for image segmentation. Then, the color, shape, and texture features of the segmented images were extracted, and then classed via several methods including Probabilistic ANNs MLP, Radial Basis Function (RBF), and Generalized Regression. Among these methods, the highest accuracy level was scored by RBF.

Owomugisha and Mwebaze [17] identified plant diseases using a method that employs leaf images. There were five disorders and five disease development phases to be identified using their proposed method. Features in the images were extracted using color and ORB feature transformations, and the obtained features were fed to an SVM classifier. The authors additionally introduced a mobile application hosted on a remote server

Gupta [18] proposed the use of image processing and a classifier called SVM-Cuckoo Search classifier to detect plant diseases. Images of sickly plant parts were used in this study. In order to enhance the contrast of the images, the author employed histogram equalization method. Segmentation of the images was carried out using K-means clustering data were partitioned using. The classifier employed in this study achieved 95% accuracy rate in data analysis.

3 Methodology

Fig leaf disease detection and classification were demonstrated in this study, the proposed approach, utilizing a novel SVM and image processing, followed a sequence of steps for the detection and classification of fig leaf disease. These steps included image acquisition, image denoising through the mean function, and image enhancement using the contrast limited adaptive histogram equalization (CLAHE) method, utilizing the FCM algorithm for image segmentation, performing feature extraction through PCA, and employing diseases classification using particle swarm optimization (PSO) SVM, backpropagation neural network (BPNN), and random forest algorithms. Figure 2 shows the steps of the proposed approach.

Figure 2 Processing image-enabled methodology for detection and classification of fig leaf disease.
Figure 2

Processing image-enabled methodology for detection and classification of fig leaf disease.

Adaptive median filtering has proven its great ability in denoising images. It is also able to identify the image pixels affected by impulse noise, allowing the determination of the correct action. Impulsive noise is caused by the presence of misaligned pixels in an image in substantial percentage. Additionally, the noise-free pixels within the vicinity are replaced with the median value derived from nearby noise-free pixels [19].

Identification of image can be eased by background extraction that does not impair the quality of the image. CLAHE was used in this study to produce pixel value histograms and the neighboring region’s value histograms. CLAHE limits the highest contrast alteration to the local histogram summit which becomes the highest contrast enhancement factor, achieved through the specification of the clip level that denotes the maximum, increasing image clarity. The clarity that is produced by CLAHE makes the method commonly used in mammograms as it increases the clarity of the small details [20]. CLAHE also allows easy distinction between the signal and the noise, but it should be noted that CLAHE causes images to be grainy.

Clustering is performed following the value intensity of the pixels, whereby the image’s preprocessed pixel values are divided into a number of classes, and so, pixels in similar class become comparable. On the other hand, pixels in different classes are not comparable. Clusters can be subsets of larger dataset and there are many clustering algorithms. The subsets, which can either be fuzzy or crisp, are used in determining the clustering method’s classification. Fuzzy clustering algorithms are generally appropriate for clustering tasks. For instance, Fuzzy C-Means (FCM) algorithm is able to split images into various clusters that overlap with other clusters at some degree. In image processing, FCM algorithm was used in this study in finding object clusters inside an image. FCM algorithm was improved in this study by including a spatial element, which resulted in increased accuracy in noisy image clustering [21].

Haar wavelet transformation is a simple wavelets transform [22], the Haar transform serves as the sampling procedure for all wavelet transformations. The Haar transform reduces a signal by half. Additionally, the use of PSO SVM eases and speeds up the classification of binary linear, in target group determination. Each data is denoted by a point or a dot, and the data will be expanded by its own cultural diversity. In determining the location allocation of the target class, the additional instances were used. In dealing with unlabeled input datasets, the use of SVM algorithms is appropriate because these algorithms are classed as a non-linear classification method [23]. Nonetheless, unsupervised learning approach was used in this study as there were no objective classes to be allocated to the instances. Additionally, function-based clusters can be formed through the addition of more instances.

Backpropagation technique is a form of learning algorithm created by Haykin and Anderson, and this technique encompasses a learning process. BPN can be used in simple pattern recognition and mapping tasks. Meanwhile, a training pair comprises an input and a target [24], and algorithm examples are used in network training, particularly for producing correct output for each input pattern. The network weights are altered when needs arise.

Random Forest is a decision-tree-based classifier that has been frequently used in classification tasks. The model trees are formed using the data’s bootstrap sample and the features’ random sampling. As for the creation of trees, it can be achieved using bagging and random selection. During forest development, the accuracy of class prediction by the trees is substantially impacted by the relationship between the pairs of tree. There may be error rates, but this strategy can rank the issues from regression and classification naturally [25].

4 Results and discussion

This study harnessed a dataset comprising 440 images, with 260 depicting diseased fig leaves and 180 featuring healthy fig leaves. Among these, 260 images were allocated for training the machine learning classifiers. The preprocessing pipeline encompassed noise reduction using the mean function and subsequent image enhancement through CLAHE. These enhanced images were then subjected to segmentation via the FCM algorithm, followed by feature extraction using PCA. Subsequently, the extracted features underwent classification using PSO SVM, BPNN, and Random Forest algorithms, culminating in disease detection.

In the context of our results, it became evident that the PSO SVM algorithm outperformed both BPNN and Random Forest in the accurate detection and classification of fig leaf disease. Although detailed efficiency parameters are omitted here, the PSO SVM algorithm demonstrated superior performance. This exceptional performance can be attributed to its aptitude for handling complex, high-dimensional data, making it a promising candidate for practical implementation in agricultural disease management.

The discussion surrounding these results highlights the significance of advanced image processing techniques, particularly the PSO SVM algorithm, in revolutionizing plant disease detection and classification. This advancement offers potential benefits for agriculture by enabling early disease diagnosis, timely intervention, and enhanced crop protection. While further validation and real-world testing are essential, our findings underscore the promise of these technologies in contributing to global food security and sustainable agricultural practices.

This study employed the following parameters to make comparison of results. Details are provided in Figures 3–7.

Accuracy = ( TP + TN ) ( TP + TN + FP + FN )

Sensitivity = ( TP ) ( TP + FN )

Specificity = ( TN ) ( TN + FP )

Precision = ( TP ) ( TP + FP )

Recall = ( TP ) ( TP + FN ) .

As can be referred above: “TP = True Positive, TN = True Negative, FP = False Positive, and FN = False Negative.”

Figure 3 “Accuracy of classifiers for fig leaf disease classification.”
Figure 3

“Accuracy of classifiers for fig leaf disease classification.”

Figure 4 “Sensitivity of classifiers for fig leaf disease classification.”
Figure 4

“Sensitivity of classifiers for fig leaf disease classification.”

Figure 5 “Specificity of classifiers for fig leaf disease classification.”
Figure 5

“Specificity of classifiers for fig leaf disease classification.”

Figure 6 “Precision of classifiers for fig leaf disease classification.”
Figure 6

“Precision of classifiers for fig leaf disease classification.”

Figure 7 “Recall of classifiers for fig leaf disease classification.”
Figure 7

“Recall of classifiers for fig leaf disease classification.”

5 Conclusion

In conclusion, agricultural image processing represents a dynamic and rapidly evolving technology, offering accelerated solutions to agricultural challenges. The significance of regular plant health monitoring and early disease detection cannot be overstated, bearing the potential to avert more severe agricultural crises. Effective classification and diagnosis of crop diseases hold substantial promise for enhancing the success of agricultural endeavors.

This study introduced a novel classification approach, tailored specifically for fig leaf disease detection, employing a new SVM and advanced image processing techniques. The computational capabilities of image processing were harnessed to execute a comprehensive pipeline, encompassing image acquisition, denoising, enhancement, segmentation, feature extraction, and disease classification. Techniques such as the mean function, CLAHE, FCM algorithm, PCA, and machine learning algorithms, including PSO SVM, BPNN, and Random Forest, were incorporated. Our results prominently highlight the exceptional accuracy of PSO SVM in classifying and detecting fig leaf diseases.

Looking ahead, the future scope of this research extends to broader applications in agricultural disease management. This encompasses the development of real-time disease monitoring systems, the integration of remote sensing technologies, and the adaptation of these techniques to diverse crops and diseases. These initiatives are essential for realizing the full potential of these advancements in diverse agricultural contexts.

However, it is crucial to acknowledge the limitations of this work. This study primarily concentrates on fig leaf disease detection, necessitating further exploration of the generalizability of the proposed approach to other plant diseases and crops. The real-world feasibility and scalability of this approach need validation, while the computational requirements may pose challenges in resource-constrained agricultural settings.

In summary, while this study marks a promising stride in agricultural disease classification, it serves as a stepping-stone for ongoing research and refinement in the realm of agricultural image processing. Addressing these limitations and continually advancing these techniques will facilitate their practical implementation, ultimately enhancing agricultural productivity, sustainability, and resilience in the face of plant diseases.

  1. Funding information: Authors state no funding involved.

  2. Conflict of interest: Authors state no conflict of interest.

  3. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-07-23
Revised: 2023-10-02
Accepted: 2023-10-05
Published Online: 2023-11-24

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  245. Animal Sciences
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  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
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  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0764
Keywords for this article
artificial intelligence; classification; fig leaf disease; image processing; machine learning techniques.
Creative Commons
BY 4.0

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  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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