Background: In the domain of grape farming, the enduring difficulties of preventing germs and viruses pose significant risks to economic viability. Modern advances in artificial intelligence, machine learning, deep learning (AI, ML, and DL), and computer vision have produced efficient methods for identifying and classifying grape viral infections. 
 
Methods: The development and improvement of deep learning algorithms designed particularly for identifying and categorizing grape leaf infections is the focus of this study. Utilizing deep intelligence techniques, five important predetermined Deep learning algorithms were used: DenseNet121, VGG19, VGG16, InceptionV3 and ResNet50V2. 
 
Result: Comparing the training accuracy, validation accuracy, training loss and validation loss of these five deep learning models, Densenet121 model has shown best performance. Densenet121 model achieved a recall and accuracy score of 99.86%. These findings demonstrate the immense scope of our method for actual use in the production of grape leaf, offering a cheaper and more feasible method for preventing disease and minimizing monetary harm.

A Comparative Study and Optimization of Deep Learning Models for Grape Leaf Disease Identification

Background: The scarcity of publicly available datasets for the detection of cashew leaf disease (DCLD) (Anacardium occidentale L.). This study represents the development and evaluation of cashew leaf disease dataset of three districts. Here, Dataset_1 with 1274 original images and Dataset_2 with 2548 augmented images to increase its diversity. Images were capture using smartphone with natural field conditions, background, various resolutions, angles, Pre-processing and data augmentation techniques were applied to enhance model performance. 
 
Methods: Multiple deep learning models, including DenseNet121, EfficientNetB0, restNet50 and Vision Transformer were trained using transfer learning to classify four cashew leaf condition: Anthracnose, Healthy, Leaves Spot and Powdery Mildew. 
 
Result: DenseNet121 achieved high class wise precision and recall, with overall accuracy between 96% to 97%. EfficientNetB0 improved upon this result, attaining overall accuracy between 96% and 98% with F-1 score up to 97%. RestNet50 and ViTB16 achieved the highest performance, with over accuracy to 99.93% respectively. 
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Generation and Utilization of an Augmented Cashew Leaf Dataset for Disease Analysis using Transfer Learning

Background: Agriculture is a major driver of economic expansion and accounts for 17% of India&rsquo;s GDP. However, the loss of agricultural land is a major problem that affects the economy and farmers&rsquo; livelihoods. Farmers use non-scientific techniques to detect pomegranate diseases, which takes time. A detection and classification system based on DL provides a quicker and more precise answer. 
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Methods: This paper presents a deep learning-based framework for the categorization and detection of pomegranate fruit diseases. For disease identification, five Deep CNN models are used: VGG16, MobileNetV2, ResNet50V2, DenseNet121 and the suggested PomeNetV2. 5,099 photos from five classes in a custom pomegranate fruit dataset are used. Pre-processing methods like data augmentation, resizing and rescaling are used to improve model performance and reduce overfitting. After that, the dataset is divided into 20% for testing and 80% for training. 
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Result: The confusion matrix, precision, recall, accuracy and F1-score were used to evaluate the performance of the suggested models. DenseNet201 did marginally better with 98% accuracy, according to the data, while VGG16, MobileNetV2 and ResNet50V2 all attained 97% accuracy. The PomeNetV2 model achieved the greatest accuracy of 99.02%, outperforming all other models.

A Comparative Study and Optimization of Deep Learning Models for Grape Leaf Disease Identification

Generation and Utilization of an Augmented Cashew Leaf Dataset for Disease Analysis using Transfer Learning

Performance Evaluation of Deep Learning Models for Multiclass Disease Detection in Pomegranate Fruits