  In this paper, we present an efficient solution for weed classification in
agriculture. We focus on optimizing model performance at inference while
respecting the constraints of the agricultural domain. We propose a Quantized
Deep Neural Network model that classifies a dataset of 9 weed classes using
8-bit integer (int8) quantization, a departure from standard 32-bit floating
point (fp32) models. Recognizing the hardware resource limitations in
agriculture, our model balances model size, inference time, and accuracy,
aligning with practical requirements. We evaluate the approach on ResNet-50 and
InceptionV3 architectures, comparing their performance against their int8
quantized versions. Transfer learning and fine-tuning are applied using the
DeepWeeds dataset. The results show staggering model size and inference time
reductions while maintaining accuracy in real-world production scenarios like
Desktop, Mobile and Raspberry Pi. Our work sheds light on a promising direction
for efficient AI in agriculture, holding potential for broader applications.
  Code: https://github.com/parikshit14/QNN-for-weed
