Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. 
 
Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. 
 
Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.

An Evaluation of Various Machine Learning Approaches for Detecting Leaf Diseases in Agriculture

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%.

An Evaluation of Various Machine Learning Approaches for Detecting Leaf Diseases in Agriculture

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