Classification of Weed Species Using Deep Learning

Author(s): Pokala PranayKumar, Raul Villamarin Rodriguezm

Abstract

Automatic identification and classification of weed species are essential in the agricultural field for controlling weed species. Weeds are an undesirable and unfortunate plant that meddles with the usage of land and water assets and along these lines unfavourably influence crop creation and human government assistance. So, identification and classification of weed are important for farmers to protect the crop field and to maintain the productivity and quality of the crop. But it takes a long time and huge human- effort to manually identify and classify weed species. Technology advancement has made complex problems to solve more efficiently and reduces manpower and lowers the costs. With technological advancement, many methods have been introduced. Using deep learning methods such as neural networks on agricultural data has increased enormous consideration lately. The evolution of deep learning made it easy for identifying and classifying the weed type. This paper uses publicly available large multiclass image datasets of weed species obtained from Australian rangelands for classification. In this paper, we are using the Transfer Learning technique with a pretrained network called resnet18 to classify the type of weed from the images present in the dataset and also calculating performance metrics like accuracy, sensitivity, recall, precision, etc. This helps in controlling weed species in the crop field.

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