Background: Agriculture is very important for human existence since time immemorial. Approximately sixty percent of the world&rsquo;s population is dependent on agriculture and its allied activities. Due to plant diseases every year farmers bear heavy economic loss which can be reduced by having an early detection system for plant disease. Conventional plant disease detection techniques are laborious and based on chemical and analytical testing. In this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network. 
 
Methods: Modifications specifically designed for the classification of plant diseases were applied to the VGG19 model. By first freezing the bottom layers of the pre-trained VGG19 model-which had been trained on the ImageNet dataset-and then fine-tuning the upper layers to better fit the PlantVillage dataset, transfer learning was used. Resizing, standardization and data augmentation are a few of the image preprocessing approaches that were used to increase the variety of the dataset and boost model performance. The efficacy of the model was assessed through the use of metrics like F1-score, recall, accuracy and precision. 
 
Result: The constructed model performed well in the classification of plant diseases, with over 95% accuracy on the test set. The success of the model in generalizing across different plant disease categories was largely attributed to the application of transfer learning and data augmentation. The findings show that deep learning techniques-in particular, the use of VGG19-can significantly enhance agricultural practices and decision-making processes by helping to quickly and accurately identify plant illnesses. VGG9-based image processing offers an accurate and automated solution for plant disease identification, achieving 98% accuracy in classifying healthy and diseased leaves. By integrating mobile applications, drones and smart farming cameras, farmers can detect diseases early and take timely action, improving crop health and yield. Future advancements in dataset expansion and real-time processing will further enhance its effectiveness in precision agriculture. 
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Deep Learning VGG19 Model for Precise Plant Disease Detection

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: Automatic feature extraction using convolutional neural networks has proven to be useful for a variety of computer vision tasks. Disease detection in plants is one such task that can be performed using convolutional neural network. Precise and timely disease detection in plants is crucial for better crop yield. So, state of the art technologies like convolutional neural network can help in developing efficient applications for this purpose. 
 
Methods: Here, we have performed an empirical study of five convolutional neural network architectures namely, VGG19, ResNet152V2, InceptionV3, MobileNetV2 and DenseNet201 for detecting diseases in tea leaves. Tea leaves affected with gray blight, red spot, brown blight, algal spot and helopeltis disease were used for the study. We have employed transfer learning models to address the issue of requiring a large number of data samples for training a convolutional neural network. The models were ranked based on their performances. We also proposed an enhanced DenseNet201 (E-DenseNet201) model by integrating channel attention module with DenseNet201 and compared its performance with the convolutional neural network architectures used here. 
 
Result: DenseNet201 demonstrated the highest performance among the five models with precision and recall value of 95.97% and 95.49% respectively. Further improvements were observed in the performance of E-DenseNet201 with precision and recall value of 98.15% and 97.5%.

Deep Learning VGG19 Model for Precise Plant Disease Detection

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

E-DenseNet201: An Enhanced Method for Detecting Diseases in Tea Leaves