# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 2 Related Work
Cotton Leaf Curl Virus (CLCuv) presents substantial risks to cotton production, requiring prompt and precise disease identification. [PERSON] et al. [20] have successfully used deep learning (DL) to classify lesions in cotton leaves, proving its potential in diagnosing agricultural pests and diseases. The study used a processing pipeline for analyzing images in natural field conditions, including acquisition, pre-processing, and attribute extraction. Four machine-learning algorithms were tested, with deep-learning models replaced in steps III and IV. Two deep convolutional network models, ResNet50 and GoogleNet, based on convolutional neural networks, attained precisions of 89.2% and 86.6%, respectively. Unlike more traditional approaches for image processing, including Closest k-neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), and neuro-fuzzy (NFC), convolutional neural networks (CNNs) have demonstrated an improvement in precision by as much as 25%. This indicates that the adoption of this approach may enhance the efficiency and reliability of inspecting plants in agricultural settings. The study's limitations encompass the considerable influence of typical image processing algorithms on outcomes, covering type, quality, descriptors, and resolution. [PERSON] et al. [21] propose a novel approach that employs an architecture for DL with serially fused features and optimal feature selection. The proposed design includes the construction of a cotton diseases-focused self-collected dataset, augmentation of this dataset with other data, extraction and computation of 3 rd and 4 th layer features using a pre-trained DL model called ResNet101, and inclusion into a single matrix. The optimal points are subsequently selected for further recognition using a genetic algorithm, which guarantees effective training and recognition. A Cubic SVM model was employed for final recognition and validated on a curated dataset. The freshly constructed dataset attained a maximum accuracy of 98.8% with Cubic SVM, demonstrating the efficacy of the suggested framework. The authors have no conflicts of interest regarding the present study. Table 1 provides a summary of the existing approaches for plant disease detection based on cotton leaf disease datasets. The limitations in the present approaches have led to the development of a new approach for cotton leaf disease detection to address limitations in current models, which often struggle with accuracy, scalability, and practical application in real-world settings. Existing techniques, primarily reliant on deep learning models like CNNs and transfer learning, demonstrate high accuracy in controlled environments but face challenges when applied in diverse field conditions, where variations in lighting, background, and disease manifestations can impact performance. Many of these models are also computationally intensive, making them impractical for deployment in resource-constrained environments such as small farms. Additionally, traditional models rely heavily on manual inspection, which is laborious, error-prone, and requires extensive experience to accurately identify disease symptoms, potentially leading to delayed intervention and crop losses. Our proposed hybrid approach--leveraging BERT for image segmentation, ResNet for feature extraction, and Particle Swarm Optimization (PSO) for parameter tuning aims to create a robust, efficient, and scalable solution. This novel model integrates the strengths of deep learning architectures with optimization techniques to improve detection accuracy,  
adaptability to varied environmental conditions, and applicability in real-time scenarios, ultimately supporting cotton farmers in preserving yield and quality.