Development of a crop monitoring system using computer vision and machine learning techniques

Abstract

The growing global population demands increased agricultural production, necessitating the implementation of smart farming practices. The development of an automated crop monitoring system using computer vision and machine learning techniques can help to reduce the manual labor involved in crop management and enhance crop yield. This paper proposes a crop monitoring system that utilizes a camera mounted on a mobile robotic platform to capture images of crops at regular intervals. The images are analyzed using computer vision algorithms to detect and track plant growth, pest infestations, and nutrient deficiencies. Machine learning techniques are then applied to the data to predict crop yield. The system is designed to be scalable and can be deployed on a variety of crops, making it suitable for use in large-scale agricultural operations. Preliminary results demonstrate the system's effectiveness in detecting plant growth with an overall accuracy rate of 95%. The proposed system has the potential to significantly improve crop management practices and increase crop yield, thereby contributing to sustainable agriculture development.

Code and data availability

The paper describes a crop/weed CNN trained on synthetic images from real field data, but contains no data availability statement, repository deposit, or public URL for the image dataset, code, or trained model. All URLs in the text are the article DOI, license, or cited references.

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