Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

Abstract

Objectives:To propose a new model for early detection and classification of apple leaf diseases by means of genetic algorithms and advanced deep learning methods in order to minimize plant degradation and maximize detection accuracy. Methods: A feature selection and extraction task is carried out by using an Improvised Flower Pollination Technique (IFPA) based genetic algorithm. Deep learning techniques such as Multi Class Support Vector Machine (MC-SVM) and Spectral Vegetation Indices (SVI) are used to classify and detect disease. The Region Growing Algorithm (RGA) and the Stochastic Gradient Descent Method (SGDM) are used to detect leaf colour segments and identify the disease at an early stage based on shape, texture, and colour features. IFPA-GA with MC-SVM and SVI extracts the features from the apple leaf dataset (collected from the Apple Experiment Station of Northwest A&F University, China) and then selects the appropriate function to perform classification and detection of disease at early occurrence and to improve the accuracy level. To demonstrate the efficiency of the proposed algorithm, MATLAB is used for implementation. The performance results are evaluated and compared to the existing models such as Faster R-CNN, R-SSD, and INAR-SSD. Findings: Early disease detection and classification of leaf diseases are achieved with 94.09% accuracy level, 93.07% Speed, 94.01% sensitivity, 93.38% specificity, 95. 01% precision, 93.17% recall, 190 True Positive, 110 True Negative, and 92.07% F-Score to detect the disease and classify it in an optimized manner, which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed detection and classification method IFPA-GA with SVM-SVI outperforms Faster R-CNN, R-SSD, and INAR-SSD in terms of accuracy and speed of apple leaf disease detection at an early occurrence by classifying it in a robust manner.

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

“…This section clearly demonstrates the implementation results of the unique ML and DL technique EPF-PSM with 2LC in comparison to the baseline versions IPFA-GA with SVM-SVI (3) , R-SSD (17) , FR-CNN (18) and INARSSD (19) . The proposed model shows evident results and overcomes the drawbacks in terms of accurate classification and ALD prediction in the premature state.…”

“…The recommended ML method EPF-PSM with 2LC was compared against the prevailing approaches IPFA-GA with SVM-SVI (3), R-SSD (17) ,FR-CNN (18) and INARSSD (19) which were selected as the existing methods in the preceding section. To assess the performance of EPF-PSM the following formula were used.…”

“…Biotic and abiotic methods were used along with SGDM to spot the ALD. 90% accuracy is achieved, and the system has drawbacks in spotting the ALD at an early occurrence (3). The E-IWDARP method was proposed to boost accuracy, as the error detection rate of this algorithm is remarkable.…”

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

“…to achieve the target values. A software defect prediction model [11] was proposed to show the unbalanced classification impact using Google-Net, VGG-Net, DenseNet-121, and RSS-IO data. The classification imbalance is showcased clearly to refine the model for defect prediction in a robust way.…”

A Dual-Branch Model Integrating CNN and Swin Transformer for Efficient Apple Leaf Disease Classification

“…The development of machine learning has provided effective solutions to the problem of disease classification, including Support Vector Machine (SVM) [2], K-Nearest Neighbors (K-NN) [3], Random Forest [4], Genetic Algorithms [5], and Principal Component Analysis (PCA) [6], which have been employed for disease classification in the past few years.…”