Plant disease detection and classification techniques: a comparative study of the performances

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 2, 2024

Abstract One of the essential components human civilization is agriculture. It helps economy in addition to supplying food. Plant leaves or crops are vulnerable different diseases during agricultural cultivation. The halt growth their respective species. Early and precise detection classification may reduce chance additional damage plants. these have become serious problems. Farmers’ typical way predicting classifying plant leaf can be boring erroneous. Problems arise when attempting predict types manually. inability detect classify quickly result destruction crop plants, resulting a significant decrease products. Farmers that use computerized image processing methods fields losses increase productivity. Numerous techniques been adopted applied based on images infected crops. Researchers made progress past by exploring various techniques. However, improvements required as reviews, new advancements, discussions. technology significantly production all around world. Previous research has determined robustness deep learning (DL) machine (ML) such k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial (ANN), convolutional (CNN), so on. Here, from DL ML included this particular study, CNNs often favored choice for due inherent capacity autonomously acquire pertinent features grasp spatial hierarchies. Nevertheless, selection between conventional hinges upon problem, accessibility data, computational capabilities accessible. Accordingly, numerous advanced tasks, DL, mainly through CNNs, preferred ample data resources available show good effects datasets, but not other datasets. Finally, paper, author aims keep future researchers up-to-date with performances, evaluation metrics, results previously used forms using image-processing intelligence (AI) field.

Language: Английский

---

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2023, Volume and Issue: 77(3), P. 3969 - 3992

Published: Jan. 1, 2023

Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers efficient disease classification to overcome this. The Convn kernels used proposed (DTomatoDNet) is 1 × 1, which reduces number of parameters helps more detailed descriptive feature extraction classification. DTomatoDNet model trained from scratch determine success rate. 10,000 images (1000 per class) publicly accessible dataset, covering one healthy category nine categories, are utilized training approach. More specifically, we classified into Target Spot (TS), Early Blight (EB), Late (LB), Bacterial (BS), Leaf Mold (LM), Yellow Curl Virus (YLCV), Septoria (SLS), Spider Mites (SM), Mosaic (MV), Healthy (H). approach obtains accuracy 99.34%, demonstrating differentiating between could be mobile platforms because it designed with fewer layers. farmers can utilize methodology detect quickly easily once been integrated by developing application.

Language: Английский

---

Unveiling the Future of Agriculture: Transformative Impact of Advanced Deep Learning with Mobile App Technology for Plant Leaf Disease Detection

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 44 - 59

Published: Jan. 1, 2025

Language: Английский

---

Intelligent contextual attention mechanism of region of interest based network model for leaf disease segmentation and classification

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107630 - 107630

Published: Jan. 31, 2025

Language: Английский

---

AI‐Powered Precision in Diagnosing Tomato Leaf Diseases

Complexity, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Correct detection of plant diseases is critical for enhancing crop yield and quality. Conventional methods, such as visual inspection microscopic analysis, are typically labor‐intensive, subjective, vulnerable to human error, making them infeasible extensive monitoring. In this study, we propose a novel technique detect tomato leaf effectively efficiently through pipeline four stages. First, image enhancement techniques deal with problems illumination noise recover the details clearly accurately possible. Subsequently, regions interest (ROIs), containing possible symptoms disease, captured. The ROIs then fed into K‐means clustering, which can separate sections based on health allowing diagnosis multiple diseases. After that, hybrid feature extraction approach taking advantage three methods proposed. A discrete wavelet transform (DWT) extracts hidden abstract textures in diseased zones by breaking down pixel values images various frequency ranges. Through spatial relation analysis pixels, gray level co‐occurrence matrix (GLCM) extremely valuable delivering texture patterns correlation specific ailments. Principal component (PCA) dimensionality reduction, selection, redundancy elimination. We collected 9014 samples from publicly available repositories; dataset allows us have diverse representative collection images. study addresses main diseases: curl virus, bacterial spot, late blight, Septoria spot. To rigorously evaluate model, split 70%, 10%, 20% training, validation, testing subsets, respectively. proposed was able achieve fantastic accuracy 99.97%, higher than current approaches. high precision achieved emphasizes promising implications incorporating DWT, PCA, GLCM, ANN an automated system diseases, offering powerful solution farmers managing efficiently.

Language: Английский

---

Cotton and Soybean Plant Leaf Dataset Generation for Multiclass Disease Classification

Journal of Phytopathology, Journal Year: 2025, Volume and Issue: 173(2)

Published: March 1, 2025

ABSTRACT Cotton and soybeans are important crops for the country's economic growth. Due to rapid spread of disease, plants susceptible bacterial viral diseases. Early identification classification using machine or deep learning models aid farmers in reducing potential losses. Model‐based detection necessitates a large number training samples high‐quality images. Thus, this study generates new datasets diagnose soybean cotton plant The images collected with help Central Institute Research (CICR) Nagpur, Maharashtra, create clean comprehensive dataset research purposes. contains 5200 images, including both diseased healthy labelled Robo flow tool, masked Photoshop tool stored dataset. generated is examined through pre‐processing novel proposed algorithms. Initially, Gabor filter used eliminate unwanted noise from Afterwards, Position attention‐based capsule network (PA‐CapNet) model perform multidisease datasets. Finally, performances assessed by evaluating varied metrics. result analysis shows that method obtains better results than other existing models. an accuracy 98% 96.89%

Language: Английский

---

Artificial intelligence for sustainable farming with dual branch convolutional graph attention networks in rice leaf disease detection

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 27, 2025

Rice is susceptible to various diseases, including brown spot, hispa, leaf smut, bacterial blight, and blast, all of which can negatively impact crop yields. Current disease detection methods encounter several challenges, such as reliance on a single dataset that diminishes accuracy, the use complex models, limitations posed by small datasets hinder performance. To overcome these this paper presents novel hybrid deep learning (DL) approach for classifying rice diseases. The proposed model leverages two distinct datasets: Leaf Diseases Dataset Disease Images Dataset. It enhances image quality through advanced techniques: Upgraded Weighted Median Filtering (Up-WMF) minimize noise Aligned Gamma-based Contrast Limited Adaptive Histogram Equalization (AG-CLAHE) improve contrast. Features from images are extracted using Discrete Wavelet Transform (DWT), Gray Level Run Length Matrix (GLRLM), learning-based VGG19 features. optimize performance, most significant features selected Bio-Inspired Artificial Hummingbird (BI-AHB) method, streamlines complexity. Classification diseases conducted new known Dual Branch Convolutional Graph Attention Neural Network (DB-CGANNet). This demonstrates remarkable achieving 98.9% accuracy 99.08% image, surpassing existing techniques. methodology facilitating improved management crops contributing increased agricultural productivity.

Language: Английский

---

Double-truncated version of OsGADs leads to higher GABA accumulation and stronger stress tolerance in Oryza sativa L. var. japonica

Plant Cell Reports, Journal Year: 2025, Volume and Issue: 44(5)

Published: April 8, 2025

Abstract Key message Calmodulin binding domain truncation from OsGAD1 and OsGAD3 resulted in enhanced GABA accumulation, upregulated stress related genes, improved tolerance to multiple abiotic stresses. Rice ( Oryza sativa L.), a critical crop for global food security, faces significant challenges Gamma-aminobutyric acid (GABA), synthesized by glutamate decarboxylase (GAD), plays vital role tolerance. Truncating the calmodulin-binding (CaMBD) GAD enzymes enhances activity production. In this study, we developed hybrid line, Hybrid #78, crossing two genome-edited lines, OsGAD1ΔC #5 OsGAD3ΔC #8, with truncated CaMBD OsGAD3, respectively. #78 demonstrated significantly survival rates cold (25%), salinity (33%), flooding (83%), drought (83%) conditions, compared wild-type Nipponbare (0–33%), OsGAD1∆C (0–66%), OsGAD3∆C #8 (0–50%). showed highest levels during stress, increases of 3.5-fold (cold), 3.9-fold (salinity), 5-fold (flooding), (drought) relative up 2-fold higher than that parent lines. RNA-seq analysis shoot tissues control conditions identified 975 differentially expressed genes between Nipponbare, 450 uniquely hybrid. Kyoto Encyclopedia Genes Genomes (KEGG) enrichment revealed upregulation nitrogen metabolism pathways likely contributes synthesis via increased also broader gene expression variability, suggesting adaptability especially stress-related such as OsDREB , OsHSP70 OsNAC3 . These findings highlight potential develop rice lines accumulation resilience

Language: Английский

---

TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 10, 2025

Language: Английский

---

A twin CNN-based framework for optimized rice leaf disease classification with feature fusion

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 11, 2025

Language: Английский

---