Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems

Abstract

Effective weed management is crucial for maintaining optimal crop growth and achieve higher yield. Recent advancement in robotic technologies and advanced deep learning (DL) models is shaping the future of robotic weed control systems. However, DL models for weed identification requires substantial amount of data collected in natural field conditions. This article presents red, green, and blue (RGB) datasets for multiple weed species found across different crop production systems. DL models require sophisticated datasets for training the model to achieve high object detection accuracy. To achieve this, a real field dataset was collected under diverse environmental conditions to mimic the natural environment and exhibits the variability in datasets. This aims to improve the accuracy of deep learning models for real time weed identification in precision agriculture. The dataset presented in this article was collected using Canon RGB camera, mounted on the front of remote-controlled robotic platform. This dataset comprises 1120 labelled images presenting five species of weeds and eight different crop species. This resource can be utilized by researchers, educators, and students in developing DL models for weed identification. The dataset can be further enriched by combining it with other relevant weed-crop datasets to create more diverse and robust datasets. This will enhance the capabilities of DL algorithms to be integrated with robotic weed control platforms for precision weed management.