Introduction Technology is becoming essential in agriculture, especially with the growth of smart devices and edge computing. These tools help boost productivity by automating tasks and allowing real-time analysis on devices with limited memory and resources. However, many current models struggle with accuracy, size, and speed particularly when handling multi-label classification problems. Methods This paper proposes a Convolutional Neural Network with Squeeze and Excitation Enabled Identity Blocks (CNN-SEEIB), a hybrid CNN-based deep learning architecture for multi-label classification of plant diseases. CNN-SEEIB incorporates an attention mechanism in its identity blocks to leverage the visual attention that enhances the classification performance and computational efficiency. PlantVillage dataset containing 38 classes of diseased crop leaves alongside healthy leaves, totaling 54,305 images, is utilized for experimentation. Results CNN-SEEIB achieved a classification accuracy of 99.79%, precision of 0.9970, recall of 0.9972, and an F1 score of 0.9971. In addition, the model attained an inference time of 64 milliseconds per image, making it suitable for real-time deployment. The performance of CNNSEEIB is benchmarked against the state-of-the-art deep learning architectures, and resource utilization metrics such as CPU/GPU usage and power consumption are also reported, highlighting the model’s efficiency. Discussion The proposed architecture is also validated on a potato leaf disease dataset of 4,062 images from Central Punjab, Pakistan, achieving a 97.77% accuracy in classifying Healthy, Early Blight, and Late Blight classes.
Karthikeyan et al. (Tue,) studied this question.