1Department of Computer Science and Engineering, Osmania University, Hyderabad-500 007, Telangana, India
2Department of Computer Science and Engineering Artificial Intelligence and Machine Learning, CBIT, Hyderabad-500 075, Telangana, India
*Corresponding Author: Samuel Chepuri, Department of Computer Science and Engineering, Osmania University, Hyderabad-500 007, Telangana, India, Email: drsamuelchepuri@gmail.com
Online published on 30 January, 2026.
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.
The study utilized the Inception-v3 deep learning model along with techniques such as transfer learning and hyperparameter 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.
The Inception-v3 model showed exceptional performance:
Achieved 87.52% accuracy without tuning.
Achieved 98.85% accuracy after hyper-parameter tuning, marking an improvement of ~11%. This approach also demonstrated faster 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.
Convolutional neural network (CNN), Cotton disease detection, Deep learning, Hyperparameter tuning, Inception-V3 model, Transfer learning
