Lightweight CNN SE transformer for robust weed classification with optimizer aware performance

During the early growth stages of crops, the weeds present can severely affect crop yields. Accurate and fast identification of the types of weeds present using robots can help mitigate this problem, allowing agronomists and farmers to employ more precise methods to control weed growth. In this research, we designed the custom Lightweight CNN-SE-Transformer architecture with smaller computational cost while still being able to capture a wide range of important features. This research evaluates the various convolutional network depth by increasing and decreasing the CNN layers. The model was evaluated with residuals and without residuals, skip connections to learn the ability of light weight architecture. Squeeze-and-Excitation (SE) modules in the convolutional backbone to increase the networks discriminative ability. The sequence of multiple convolutional layers with residuals helps to extract the deep feature extraction which is necessary dealing with small plants with more similarities. The main difference from previous deep learning methods is that the model is able to shrink the sequence of tokens and utilize the subsequent features to more efficiently analyze the data while still maintaining the overall performance of the model. To explicitly prove the proposed model ability, model was evaluated with two strong datasets with three class and 16 class weeds. Proposed architecture performed well on both datasets and achieves best test accuracy. For the three optimizers, Adam, RMSProp, and adam+label smoothing, the performance of each optimizer is evaluated to gain insight on the convergence and stability of the model under a wide variety of training conditions. The results across the three models have shown that Adam optimizer achieves 97% of test accuracy was the best optimizer while Adam label smoothing achieves similar to 96.5% and RMSProp achieves 95% of test accuracy shows less performance. Explainability of model in gradcam shows adam with proposed custom lightweight model understand the exact features of weed.