Skill‐Honey Badger Optimisation Algorithm‐Enabled Deep Convolutional Neural Network for Multiclass Leaf Disease Detection in Tomato Plant DOI
Naresh Kumar Trivedi, Sachin Jain, Alok Misra

и другие.

Journal of Phytopathology, Год журнала: 2024, Номер 172(6)

Опубликована: Ноя. 1, 2024

ABSTRACT In today's life, agriculture holds considerable importance in human life and the economy of a nation. Agriculture, including tomato farming, plays vital role as one most extensively consumed vegetables worldwide. However, crops are very prone to diseases, leading reduced production economic down agricultural fields. To solve these issues, an effective method is proposed named Skill‐Honey Badger Optimisation Algorithm‐enabled deep convolutional neural network (CNN) (SHBOA_DeepCNN) for detecting leaf disease plants. this method, input primarily preprocessed by utilising Savitzky–Golay (SG) filtering. Then, segmentation performed Dense‐Res‐Inception Net (DRINet), which trained using devised SHBOA. The SHBOA designed incorporating Skill Algorithm (SOA) Honey (HBA). Subsequently, image augmentation on segmented images two techniques, namely, colour position augmentation. At last, multiclass detection DeepCNN, experimental analysis SHBOA_DeepCNN showed high accuracy 91.91% true positive rate (TPR) 90.24%. Moreover, it achieved minimum false (FPR) 7.38%. code article available at “ https://github.com/Amisra‐98/SHBOA_DeepCNN.git ”.

Язык: Английский

A comprehensive review of artificial intelligence - based algorithm towards fetal facial anomalies detection (2013–2024) DOI Creative Commons
N. Sriraam,

Babu Chinta,

Suresh Seshadri

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(5)

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Innovative deep learning solutions for Turkish butterfly species identification: a VGGNet enhancement study DOI
Mustafa Teke,

Gamze Elsamoly

Signal Image and Video Processing, Год журнала: 2025, Номер 19(5)

Опубликована: Март 10, 2025

Язык: Английский

Процитировано

0
TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images DOI Creative Commons
S. Ledbin Vini,

P. Rathika

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Апрель 10, 2025

Язык: Английский

Процитировано

0
Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures DOI Creative Commons
Jatin Sharma,

Asma A. Al-Huqail,

Ahmad Almogren

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 22, 2025

Global food security depends on tomato growing, but several fungal, bacterial, and viral illnesses seriously reduce productivity quality, therefore causing major financial losses. Reducing these impacts early, exact diagnosis of diseases. This work provides a deep learning-based ensemble model for leaf disease classification combining MobileNetV2 ResNet50. To improve feature extraction, the models were tweaked by changing their output layers with GlobalAverage Pooling2D, Batch Normalization, Dropout, Dense layers. take use complimentary qualities, maps from both combined. study uses publicly available dataset Kaggle classification. Training 11,000 annotated pictures spanning 10 categories, including bacterial spot, early blight, late mold, septoria spider mites, target yellow curl virus, mosaic healthy leaves. Data preprocessing included image resizing splitting, along an 80-10-10 split, allocating 80% training, 10% testing, validation to ensure balanced evaluation. The proposed 99.91% test accuracy, suggested was quite remarkable. Furthermore, guaranteeing strong performance across all showed great precision (99.92%), recall (99.90%), F1-score 99.91%. With few misclassifications, confusion matrix verified almost flawless even further. These findings show how well learning can automate diagnosis, providing scalable accurate solution smart agriculture. By means intervention agriculture techniques, strategy has potential crop health monitoring, economic losses, encourage sustainable farming practices.

Язык: Английский

Процитировано

0
Leaf Disease Detection and Fertilizer Recommendation using Deep Learning DOI Open Access

J. Rasiga,

S. E.,

V. Srinithi

и другие.

Journal of Soft Computing Paradigm, Год журнала: 2025, Номер 7(1), С. 63 - 74

Опубликована: Март 1, 2025

Plant disease detection is an important field of study since early can drastically minimize crop losses and enhance agricultural productivity. Pathogens like fungi, bacteria, viruses are responsible for most plant diseases, which seriously affect health yield. In this research, a pre-trained convolutional neural network (CNN) algorithm, VGG 16 used to classify various leaf diseases with very high accuracy, taking advantage deep learning methods in observing visual symptoms on leaves. The model takes the input image diseased leaf, extracts hierarchical features using its multi-layered architecture, determines type disease, allowing accurate diagnosis. Moreover, system designed recommend fertilizer based identified, enabling farmers take necessary action reduce damage By combining cutting-edge AI knowledge, method presents scalable effective solution management, sustainable agriculture food security.

Язык: Английский

Процитировано

0
Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients DOI Creative Commons
Saiyi Han, Tong Zhang, W. Deng

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 16, 2025

Identifying patients suitable for conversion therapy through early non-invasive screening is crucial tailoring treatment in advanced gastric cancer (AGC). This study aimed to develop and validate a deep learning method, utilizing preoperative computed tomography (CT) images, predict the response AGC patients. retrospective involved 140 We utilized Progressive Distill (PD) methodology construct model predicting clinical based on CT images. Patients training set (n = 112) test 28) were sourced from The First Affiliated Hospital of Wenzhou Medical University between September 2017 November 2023. Our PD models' performance was compared with baseline models those Knowledge Distillation (KD), evaluation metrics including accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under curve (AUCs), heat maps. exhibited best performance, demonstrating robust discrimination an AUC 0.99 accuracy 99.11% set, 0.87 85.71% set. Sensitivity specificity 97.44% 100% respectively each suggesting absence discernible bias. method accurately predicts Further investigation warranted assess its utility alongside clinicopathological parameters.

Язык: Английский

Процитировано

0
Enhancing crop disease classification and severity assessment through Fuzzy Rank-based ensemble learning approach DOI

Yassamina Medjadba,

Hamza Drid,

Xianchuan Yu

и другие.

Signal Image and Video Processing, Год журнала: 2025, Номер 19(8)

Опубликована: Май 28, 2025

Язык: Английский

Процитировано

0
An Approach Toward Classifying Plant-Leaf Diseases and Comparisons With the Conventional Classification DOI Creative Commons
Anita Shrotriya, Akhilesh Sharma, Srikanth Prabhu

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 117379 - 117398

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

3
Detection and Categorization of Tomato Plant Diseases using A Convolutional Neural Network DOI

S. Sridevi,

S. Famila,

G. Mariammal

и другие.

Опубликована: Март 15, 2024

Early disease diagnosis and classification in tomato plants may save farmers money on crop treatments lead to enhanced food output. There has been a lot of work done by researchers categorize plant illnesses, but it is still difficult quickly identify many leaf diseases since healthy diseased regions the plant's leaves seem so similar. Convolution Neural Network (CNN) powerful deep learning (DL) approach for that we have developed overcome concerns mentioned above. The Plant Village Kaggle dataset, which often used readily accessible, was utilized. proposed method provides low-cost, image-resilient solution holds up under different lighting conditions, colors, orientations affected area. Upon evaluating suggested CNN model across parameters, attains an accuracy rate 95%.

Язык: Английский

Процитировано

2
A lightweight tomato leaf disease identification method based on shared‐twin neural networks DOI Creative Commons

Linfeng Wang,

Jiayao Liu,

Yong Liu

и другие.

IET Image Processing, Год журнала: 2024, Номер 18(9), С. 2291 - 2303

Опубликована: Май 31, 2024

Abstract Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount data, multiple training heavy computation. In this study, a lightweight shared Siamese neural network method was proposed identification, which suitable resource‐limited environments. Experiments on Plant‐Village, Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% confirms effectiveness in small data environment. addition, compared mainstream algorithms, it improves by up to 35.3%on Plant‐Village two respectively. The experimental results also still performs well imbalanced sample size small.

Язык: Английский

Процитировано

1