# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 2 Related Work
This paper examines the research on a variety of methodologies for the detection of plant leaf maladies using neural networks. It introduces a novel approach to the detection of plant maladies in plant leaf servers by employing a deep Convolutional Neural Network (ANN). The investigation precisely identifies diseases in cotton leaves by examining their consistency and the efficacy of various plant classification strategies. This model employs ANN, SVM, and NB classifiers to identify and classify leaf diseases. It offers a percentage of the impacted leaf area. This model is beneficial for agricultural purposes, including the detection of plant component diseases, particularly those impacting leaves. Improved crop yields and early detection can enhance the Indian GDP. The study's limitations include crop health, seasonal changes, environmental anomalies like disease, water shortages, and insects, population growth, and climatic conditions. Minimizing pesticide usage is crucial for cost reduction and environmental protection. [PERSON] et al. [14] developed a novel approach known as CoDet, which employs sophisticated mathematical techniques to precisely identify cotton plants. CoDet's design focuses on comprehending the growing patterns of cotton-based plants. The layers of the CNN evaluate input images and produce maps and outputs that help in classifying different regions associated with cotton plants. This approach demonstrates quick and reliable identification of cotton plants. The CoDet model architecture uses convolutional training to categorize images using teachable filters. It consists of a Reshape layer, three convolutional layers, a ReLU activation function, and a batch normalization layer. The model aims to identify patterns in input photos for cotton recognition by reducing feature maps' spatial dimensions and removing units to avoid overfitting. The proposed CoDet architecture is compact and computation-optimized, making it ideal for IoT and mobile-based applications. It offers a versatile approach for detecting cotton plants, facilitating seamless integration into diverse software and hardware systems. The existing research does not consider the specific attributes that distinguish cotton growing and its range, leading to poor identification models. [PERSON] and [PERSON] [15] present a deep-learning model for identifying diseased cotton plants using leaf images and an IoT-based platform for climate change detection. The study utilized a deep CNN model trained on both infected and healthy leaf images. The system involves collecting a dataset, pre-processing it, training the CNN, and validating the model. The dataset is collected from daily surveys using sensors on crop fields and an IoT-based system camera. Image augmentation techniques are used to reduce overfitting. The CNN is trained using various deep-learning contexts, including Python's Theano 1.0.5, Torch7, and Caffe. The study's output is based on the F1-Score performance evaluation of the model, where 1 corresponds to a perfect score, and 0 corresponds to a zero score. The research trains the model at NVIDIA Graphics Processing Unit GPU mode and gets an accuracy of approximately 98.0% with a total of 10 iterations. In the future, it may be necessary to invest considerable time in predicting the outcomes of diseases affecting the cotton plant to ensure its optimal performance. [PERSON] et al. [16] seek to develop a system for identifying diseases in cotton leaves through the analysis of digitized color images. The system uses SVM, CNN, and a Hybrid approach to predict diseases like Fusarium wilt and Leaf Curl Disease, specific to cotton plants. The process involves collecting a cotton dataset, applying different classifications, calculating performance metrics, and comparing results. The dataset consists of four classes: Leaf Curl Disease, Bacterial Blight, Healthy, and Fusarium wilt. The classification model's efficacy is assessed using a total of 1661 images.