Abstract
The infestation of weeds in cotton fields poses a serious threat to the production of lint. Weeds are eliminated either by spraying weed kill sprays, or removal by hand, or by automated machinery. Machine learning (ML) and deep learning (DL) models are used in these machines for weed classification and detection. Compared with machine learning models, DL models based on neural networks have greater ability to extract features. A spiking neural network (SNN) is used in this paper for the classification of weed images commonly found in cotton fields. The classification of the 6 classes of weeds is accomplished via transfer learning on the AkidaNet Convolutional Neural Network (CNN) model, which is converted to an SNN via software tools from BrainChip. The process includes quantization of the parameters to achieve compatibility for conversion and eventual deployment on neuromorphic devices. The classification accuracy and loss metrics are presented for the training and validation data. The accuracy and loss values achieved during training were 96.26% and 26.67% for the training data and 93.58% and 31.16% for the validation data respectively. Prior to conversion to the SNN, during quantization, the accuracy achieved is 87.96%. With the inclusion of Quantization Aware Training (QAT), the accuracy improved to 90.74%. After conversion from the CNN to the SNN, the model accuracy obtained for the testing data was 94.44%. The results demonstrate that SNN-based models can achieve good classification accuracy with reduced computational cost, making them suitable for deployment on energy-efficient neuromorphic systems. This approach holds strong potential for real-time automated weed detection in precision cotton agriculture.