In agriculture, accurate and timely detection of plant diseases is crucial for minimizing crop losses and ensuring food security. Traditional methods of plant disease detection often rely on visual inspection and single-modal data analysis, which can be limited in their diagnostic accuracy. This study introduces an innovative ensemble learning framework that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for multimodal plant disease detection to address these limitations. The proposed framework capitalizes on the strengths of both CNNs and RNNs by processing visual data from leaf images and sequential data such as time-series measurements of environmental conditions. CNNs are adept at extracting intricate spatial features from leaf images, identifying visual symptoms of diseases with high precision. Concurrently, RNNs are designed to capture temporal patterns in sequential data, providing insights into environmental factors that may influence disease development. The ensemble method employed in this study aggregates predictions from both CNN and RNN models using techniques such as majority voting and weighted averaging. Majority voting involves combining the outputs of multiple models to make a final prediction based on the most common outcome, while weighted averaging assigns different weights to the predictions of each model based on their performance, leading to a more balanced and accurate diagnostic result. Experimental evaluations were conducted on comprehensive multimodal datasets, including diverse plant species and varying environmental conditions, to assess the effectiveness of the proposed framework. The results demonstrate that the ensemble approach significantly outperforms individual CNN and RNN models, achieving higher diagnostic accuracy, precision, recall, and F1 scores. This superior performance underscores the potential of integrating diverse data streams to provide a holistic view of plant health, enabling more accurate and reliable disease diagnosis. The findings of this study highlight the importance of leveraging multimodal data and advanced machine-learning techniques in plant disease detection. By integrating spatial and temporal information, the proposed framework offers a comprehensive diagnostic tool that can be instrumental in improving agricultural practices, optimizing plant health management, and ultimately contributing to sustainable farming practices.

Plants play a crucial role in human history by providing essential resources such as food. However, they are susceptible to various diseases like leaf blight, grey spots, and rust, posing significant challenges for farmers and ranchers due to the potential damage and financial losses incurred. The current method of diagnosing plant diseases relies heavily on manual inspection by trained professionals, which becomes increasingly difficult and resource-intensive, particularly on large farms. To address this issue, a novel approach is proposed, integrating clustering methods and neural networks to enhance the efficiency of identifying and studying plant disease samples. This method aims to expedite the diagnostic process and accurately quantify disease-induced damage. It introduces a formula for assessing damage, calculating the diseased leaf area as a fraction of the total leaf area. With an impressive accuracy rate of 96–97%, the system successfully identifies diseases such as Alternaria, Anthracnose, Bacterial Blight, and Cercospora Leaf Spot. This advancement signifies a notable improvement over previous methods, highlighting the evolution of the system. By harnessing the capabilities of clustering techniques and neural networks, this innovative approach seeks to revolutionize the detection and monitoring of plant diseases in agriculture, offering a more accessible and precise solution.

This paper investigates the integration of deep learning (DL) architectures with Recurrent Neural Networks (RNNs) for automated leukemia detection in Peripheral Blood Smear (PBS) images. Models such as DenseNet201, EfficientNetB3, Inception v3, InceptionResNetV2, MobileNetV2, ResNet50, VGG16, and Xception, each enhanced with a Gated Recurrent Unit (GRU) layer, are employed to capture spatial and temporal dependencies in image data. Through extensive experimentation on a standard leukemia dataset, the performance of each model is evaluated based on accuracy and computational efficiency. Among these, the Xception + GRU model achieves the highest classification accuracy, attaining an impressive 99.69%. This exceptional result underscores the efficacy of combining Convolutional Neural Networks (CNNs) with RNNs, particularly GRUs, in the accurate detection of leukemia from PBS images. The findings offer valuable contributions to the field of medical image analysis, demonstrating the potential of DL techniques to enhance automated disease diagnosis. By advancing the precision of leukemia detection, this study provides promising implications for improving patient care and treatment outcomes.