Cotton Disease Prediction Using Deep Transfer Learning: Comparative Analysis of Resnet50, VGG16 and Inceptionv3 Models

Cotton is among the most critical crops in the world textile industry, but it is highly susceptible to a vast array of infections that have a tremendous impact on output and fiber quality. Traditional cotton disease diagnosis is mostly based on manual inspection by farmers and experts and is time consuming, labor intensive and inaccurate due to similarity of symptoms. The high rate at which artificial intelligence, especially computer vision and deep learning (DL), have advanced has provided effective alternatives to auto-detecting plant diseases. As a subdivision of the DL approach, transfer learning allows adapting existing convolutional neural networks to the agricultural domain using smaller datasets to guarantee higher performance. This work introduces comparative analysis of three popular deep transfer learning (DTL) models ResNet50, VGG16, and InceptionV3 that are used in the classification of cotton leaf diseases. The training, validation, and testing were performed on a dataset of 1,991 labelled images that included four categories of normal and diseased cotton leaves and plants. All models were optimized and assessed with standard measures, such as validation and test accuracy. The experimental results show that InceptionV3 had the highest accuracy of 95.28, VGG16 had 85.85, and ResNet50 had the lowest accuracy of 69.81. The high accuracy of InceptionV3 is also a testament to its ability in the extraction of multi-scale features, and the trade-off between accuracy and computational efficiency. The results affirm the feasibility of DTL frameworks to revolutionize precision agriculture by facilitating diagnosis of cotton diseases in a timely and reliable manner. This development can help in ensuring that farming activities are sustainable, pesticides are used efficiently and the economy does not suffer economic losses and helps in ensuring that productivity and environmental protection are maintained in cotton farming.