From recent past years, Weed identification remained a hot topic for researchers. Majority of work focused on the detection of weed but we are trying to identify the weed via weed name. The unrivaled successes of deep learning make the researchers able to evaluate different weed species in the complex rangeland climate. Nowadays, with an increasing population, farming productivity needs to be increased a lot to meet the demand for accurate weed detection. Increased demand for an increase in the use of herbicides, resulting in environmental harm. In this research work, the picture of weed helps to detect and differentiate as per area, and its name. The main aim of this research is the identification of weed so that fewer herbicides can use. This research work will contribute toreducing the higher use of herbicides by helping clear identification of weed names through its features. We use transfer learning in machine learning. The deep Weeds dataset is used for the evaluation. For this, we use the deep learning model ResNet50 to get better results. The Deep Weeds dataset contains 17,509 images that are label and eight nationally recognized species of weed belonged to 8 across northern Australia locations. This paper declares a baseline for classification performance on the dataset of weed while utilizing the deep learning model ResNet-50 and it is a benchmark too. Deep learning model ResNet-50 attained an average accuracy classification of 96.16. The findings are high enough to make effective use of weed control methods in Pakistan for futurefield implementation. The results confirm that our System offers more effective Weed recognition than many other systems.
A Deep Learning Model Compression and Ensemble Approach for Weed Detection
