Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Cerrado
Inglés
Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases.
metadata
Aggarwal, Meenakshi; Khullar, Vikas; Goyal, Nitin; Singh, Aman; Tolba, Amr; Bautista Thompson, Ernesto y Kumar, Sushil
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR
(2023)
Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification.
Agriculture, 13 (5).
p. 936.
ISSN 2077-0472
|
Texto
agriculture-13-00936.pdf Available under License Creative Commons Attribution. Descargar (1MB) | Vista Previa |
Resumen
Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | rice leaf disease; machine learning; deep learning; ensemble learning; segmentation; pre-trained models |
| Clasificación temática: | Materias > Ingeniería |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica |
| Depositado: | 17 Oct 2023 23:30 |
| Ultima Modificación: | 17 Oct 2023 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/9237 |
Acciones (logins necesarios)
 |
Ver Objeto |
<a class="ep_document_link" href="/17889/1/PIIS1879406825006344.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Introduction Cancer in older adults is often associated with functional limitations, geriatric syndromes, poor self-rated health, vulnerability, and frailty, and these conditions might worsen treatment-related side effects. Recent guidelines for patients with cancer during and after treatment have documented the beneficial effects of exercise to counteract certain side effects; however, little is known about the role of exercise during cancer treatment in older adults. Materials and Methods This is a multicentre randomised controlled trial in which 200 participants will be allocated to a control group or an intervention group (the sample size has been calculated to detect a clinical difference of 1 point in Short Physical Performance Battery (SPPB) score, assuming an α error of 0.05, a β error of 0.20, and a 10 % loss rate). Patients aged ≥70 years, diagnosed with any type of solid cancer and candidates for systemic treatment are eligible. Subjects in the intervention group are invited to participate in a 12-week supervised multicomponent exercise programme in addition to receiving usual care. Study assessments are conducted at baseline and three months. The primary outcome measure is physical function as assessed by the SPPB. Secondary outcome measures include comprehensive geriatric assessment scores (including social situation, basic and instrumental activities of daily living, cognitive function, depression, nutritional status, polypharmacy, geriatric syndromes, pain, and emotional distress), anthropometric characteristics, frailty status, physical fitness, physical activity, cognitive function, quality of life, fatigue, and nutritional status. Study assessments also include analysis of inflammatory, endocrine, and nutritional mediators in serum and plasma as potential frailty biomarkers at mRNA and protein levels and multiparametric flow cytometric analysis to measure immunosenescence markers on T and NK cells. Discussion This study seeks to extend our knowledge on exercise interventions during systemic anticancer treatment in patients over 70 years of age. Results from this research will guide the management of older adults during systemic treatment in hospitals seeking to enhance the standard of care.
Julia García-García mail , Ana Rodriguez-Larrad mail , Maren Martinez de Rituerto Zeberio mail , Jenifer Gómez Mediavilla mail , Borja López-San Vicente mail , Nuria Torrego Artola mail , Izaskun Zeberio Etxetxipia mail , Irati Garmendia mail , Ainhoa Alberro mail , David Otaegui mail , Francisco Borrego Rabasco mail , María M. Caffarel mail , Kalliopi Vrotsou mail , Jon Irazusta mail , Haritz Arrieta mail , Mireia Peláez mail mireia.pelaez@uneatlantico.es, Jon Belloso mail , Laura Basterretxea mail ,
García-García
en
close
Background Post-kala-azar dermal leishmaniasis (PKDL) is a skin condition that can become a complication in about 15 % of patients who have had kala-azar. Despite its significance, treatment options for PKDL are still limited. This systematic review and meta-analysis aim to evaluate the efficacy of amphotericin B for this condition. Methods PubMed, Embase, Cochrane, and Web of Science databases were searched for randomized controlled trials (RCTs) that reported the efficacy of Liposomal Amphotericin B in the treatment of PKDL. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Events per 100 observations with 95 % confidence intervals were performed for outcomes. Results Nine studies with 639 patients were included, the treatment durations ranging from 7 to 60 days. The mean age ranged from 9.2 to 31.0 years, and 359 patients were male. The PKDL treatment with liposomal amphotericin B resulted in a cure rate of 91.36 % (95 % CI: 76.60-97.15). However, a relapse was observed in 11.42 % (95 % CI: 6.20-20.8) of patients. Adverse events were common, with hepatic enzyme elevation (ALT/AST) being the most frequent (61.75 %; 95 % CI: 21.81–90.33), followed by fever in 29.93 % of cases (95 % CI: 5.09–77.30). Among the more serious side effects, decreased serum potassium was observed in 19.27 % (95 % CI: 3.84–58.82), and increased serum creatinine, indicative of nephrotoxicity, occurred in 15.08 % (95 % CI: 3.97–43.27). Nausea or vomiting, although less severe, affected 12.36 % of patients (95 % CI: 4.81–28.25). Conclusions These findings highlight that while liposomal amphotericin B is a potent therapeutic option for PKDL, its administration requires careful management and clinical vigilance to optimize outcomes and minimize risks.
Deivyd Vieira Silva Cavalcante mail , Lilia Maria Lima de Oliveira mail , Noor Husain mail , Beatriz Ximenes Mendes mail , Ana Clara Felix de Farias Santos mail , Luciana Borrigueiro mail , Lyria de Oliveira Rosa mail , Christian Ndikuryayo mail , Sarah Soares Amorim mail , Lalit Mohan mail , Fabiana Castro Porto Silva Lopes mail ,
Cavalcante
en
close
Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,
Luo
<a href="/17819/1/1-s2.0-S2214804325000679-main%20%281%29.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
What works in financial education? Experimental evidence on program impact
Financial education is increasingly essential for safeguarding both individual and corporate well-being. This study systematically reviews global financial education experiments using a dual-method framework that integrates a deep learning classifier with advanced multivariate statistical techniques. Our analysis indicates that while short-term improvements in financial literacy are common, such gains tend to diminish over time without ongoing reinforcement. Moreover, the limited impact of digital innovations and monetary incentives suggests that successful financial education depends on more than simply deploying technological solutions or extrinsic rewards. Overall, this review provides valuable insights into the evolving landscape of financial education in a dynamic economic context and underscores the need for sustainable strategies that secure lasting improvements in financial literacy.
Gonzalo Llamosas García mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es,
García
<a class="ep_document_link" href="/17888/1/s41598-025-28140-0_reference.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
An improved hybrid image steganography method using AES algorithm
Image steganography is the process of hiding information, which can be text, image, or video inside a cover image. Recent steganography literature hasn’t addressed the problem of loss of secret information during extraction and reliability. Hence, to reduce information loss and provide reliability between in the basic criteria, Herein, we proposed a hybrid method that utilizes the least significant bit (LSB) substitution, transppsition, magic matrix, key and Advance Encrytion Standard (AES) algorithm. The LSB method decreases embedding errors by implementing a new value difference algorithm. In addition, to improves the reliability between the basic criterion for image steganography we used transposition, magic matrix, key and AES. The proposed method ensures a high-quality image format in the RGB color model to conceal the hidden message within the cover image which is jpeg. The proposed hybrid method performed several experiments and these are mainly based on quality assessment metrics such as PSNR, SSIM, RMSE, NCC, etc. which showed better results. The proposed method also analyzed with different perspectives in terms of different dimensions of images and different sizes of message text which showed better results. In addition, the performance of the proposed method showed better results based on (regular and singular) steganalysis, noise, and cropping attacks. The security analyses such as key space, differential, and statistical attacks show that the proposed scheme is secure and robust against channel noise and JPEG compression.
Syeda Zahra Banoori mail , Wajidullah Khan mail , Shahid Rahman mail , Fahad Masood mail , Abdu Salam mail , Farhan Amin mail , Isabel de la Torre mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Helena Garay mail helena.garay@uneatlantico.es, Gyu Sang Choi mail ,
Banoori