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IndianPotatoWeeds: A Novel Dataset and its Role in Weed Detection and Management for Potato Crops

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Abstract

Weeds, which are undesirable plants growing alongside crops, pose serious risks to the global natural environment. They compete with native species, lead to land degradation, and diminish productivity in both agricultural and forested areas. This requires developing an efficient and optimal AI-based weed management and control approach. This study introduces the natural, extensive, and publicly accessible image dataset IndianPotatoWeeds, encompassing weed species in the Indian rangelands, and applies deep learning techniques to detect weeds. The dataset, comprising over 1500 RGB images, facilitates the creation of reliable detection and classification techniques for effective robotic weed management. The study evaluates the dataset’s segmentation, detection, and classification performance using state-of-the-art deep learning models YOLO-v3 and Mask RCNN. Both models performed well and achieved mean average precision (mAP@50) of 0.70 for Yolov3 and 0.75 for Mask RCNN. These results emphasize the dataset’s utility for building effective weed detection systems. This research enhances weed management strategies, resulting in better crop yields and reduced herbicide usage. It also provides a foundation for future studies by making the dataset available for further development and exploration in the field.

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Data Availability

The dataset and the source code used in this research is available online on https://www.kaggle.com/datasets/rajni88/indianpotatoweed-dataset.

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Funding

The research of the second author, i.e., Amar Nath, was partially funded by a grant from the Anusandhan National Research Foundation: Science and Engineering Research Board (ANRF-SERB), Grant No. EEQ/2023/000792.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SLIET Longowal, Sangrur, Punjab, 148106, India

    Rajni Goyal, Amar Nath &  Utkarsh Niranjan

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  1. Rajni Goyal
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  2. Amar Nath
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  3. Utkarsh Niranjan
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Contributions

R.G conceptualized the idea and did data collection, data annotation, model training, experimentation, and paper writing. A.N did project supervision, resources acquisition, methodology review & editing. U. supervised the data annotation, model training, experimentation, visualization, writing - review & editing.

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Correspondence to Rajni Goyal.

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Goyal, R., Nath, A. & Utkarsh Niranjan IndianPotatoWeeds: A Novel Dataset and its Role in Weed Detection and Management for Potato Crops. SN COMPUT. SCI. 6, 466 (2025). https://doi.org/10.1007/s42979-025-03969-4

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