Background: Cotton is an important crop globally and early detection of plant diseases is crucial for maintaining yields. Traditional methods for disease detection are manual and inefficient, highlighting the need for advanced technology like AI to enhance productivity. 
 
Methods: The study utilized the Inception-v3 deep learning model along with techniques such as transfer learning and hyper-parameter tuning. These approaches helped design an efficient system to classify whether a cotton plant is healthy or diseased. Comparisons were made with other pre-trained models like VGG16, ResNet50 and ResNet152V2. 
 
Result: The Inception-v3 model showed exceptional performance: 
&bull; Achieved 87.52% accuracy without tuning. 
&bull; Achieved 98.85% accuracy after hyper-parameter tuning, marking an improvement of ~11%. This approach also demonstrated faster&nbsp;and more precise predictions for diseases like bacterial blight, army worms and aphids. It supports sustainable farming by reducing chemical usage while maintaining crop quality and yield.

Leveraging Inception-V3 and Transfer Learning for Early Detection and Classification of Cotton Crop Diseases

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%.

Leveraging Inception-V3 and Transfer Learning for Early Detection and Classification of Cotton Crop Diseases

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