Advanced Hybrid ViT-ConvNeXt Framework for Robust Multi-Part Plant Disease Detection with Entropy-Driven Feature Selection

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques

HYBRID DEEP LEARNING MODEL FOR AUTOMATED PLANT DISEASE CLASSIFICATION

An extensive set of experiments was conducted to evaluate the performance of the proposed model for plant disease detection, and it is demonstrated that the model achieves highly reliable results, with an accuracy of 97.13%.

A Resource-Efficient Framework for Plant Disease Classification Using Classical Image Features

Plants can suffer a number of diseases that impact agricultural productivity and food security, particularly in developing farming communities. Although deep learning is capable of classification of diseases with outstanding results, its use is limited due to the difficulty of obtaining large labeled databases and the high requirement of computation. To address these challenges, this study proposes a new method for plant disease classification based on traditional image processing and machine learning algorithms with lightweight and low computation requirements. This one uses several handcrafted descriptors such as color histograms, Haralick texture features and Hu moments to retrieve the information relevant to a disease from the segmented leaf images. Performance of top five classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression and Naïve Bayes classifiers are evaluated from the dataset of healthy plant leaves and diseased plant leaves images on 10-fold cross validation. Based on the results of the research work, the best classification model was the Random Forest Classifier model with the accuracy value is 98.12%, 0.98 precision, 0.98 recall, and 0.98 F1 value. The proposed solution was also found to be uncomputation complex and low memory consuming and can be made realtime inference. Therefore, this solution can be implemented onto agricultural systems, considering edge computing and IoT. The results of the research also demonstrated that feature-based machine learning approaches afford interpretable and reliable plant disease detection at a low computation cost, further contributing to sustainable, and precision agriculture.

Multi-Plant Disease Classification using ResNet50-based Deep Ensemble Learning Framework

Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant disease detection using ResNet50 to improve classification performance across multiple crop varieties. The proposed framework takes five important categories of plants into consideration including tomato, potato, grape, apple and maize, and 10 classes of healthy and diseased plants are generated from the PlantSeg dataset. The Anaconda platform was used along with Python to create a development environment that allows data preprocessing, augmentation, training and testing to be implemented efficiently. The proposed ensemble framework combines the feature extraction power of ResNet50 with several deep learning classifiers so as to obtain a good identification performance at different resolutions and environments. The proposed model performance is tested with the following metrics Accuracy, Precision, Inference Time, and Resolution quality and compared with MobileNetV2, YOLOv8 and the baseline CNN models. Experimental results show that the proposed ensemble ResNet50 framework achieves an accuracy of 98.7% and precision of 98.3%, which is about 6.4%, 4.8%, and 9.2% higher than that of MobileNetV2, YOLOv8, and CNN respectively. Moreover, the proposed method achieves high resolution disease localization capability with an inference time improvement of almost 18% compared with YOLOv8. The proposed system greatly improves the detection accuracy of the early stage and the calculation speed of the system, which is very suitable for smart agriculture applications and real-time monitoring of the health status of crops.

AI-driven agricultural productivity: detecting potato leaf diseases with multi-scale feature extraction

A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.

Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model

The identification of plant diseases plays a crucial role in sustaining agricultural productivity and minimizing economic losses. Traditional approaches, which often depend on visual assessment and the farmer’s experience, are typically inadequate for the timely recognition of infections, allowing diseases to progress and cause substantial damage. In overcoming these challenges, deep learning methods offer greater capability in solving complex classification problems than traditional machine learning algorithms. In this study, we propose a hybrid transformer-driven framework for high-precision disease detection on mango leaves. This approach combines mango leaf vein segmentation with transformer-based feature extraction. MaxViT and Swin models derive 512 and 768 features from each image, which are then combined to form a 1280-dimensional feature vector. The feature attention mechanism highlights the most informative components of the features, while the improved grey wolf optimizer reduces the increased dimensionality. 200 discriminative features were selected from the feature vector, and the decreasing features were classified using six machine learning classifiers. Experiments were performed on the MangoLeafBD dataset, which contains eight classes: seven diseases and a healthy class. The proposed MaxViT-Swin–IGWO hybrid framework achieved remarkable results, achieving 100% accuracy for the Linear Discriminant classifier and 99.98% accuracy for the Neural Network classifier. Performance analysis was accomplished using precision, recall, F1-score, dice, and ROC criteria. Furthermore, an ablation test was conducted to evaluate the impact of individual model variations on the preprocessing pipeline. The findings revealed that the proposed MaxViT Swin–IGWO hybrid framework detects mango leaf diseases with superior performance, outperforming both conventional and contemporary alternatives.