Abstract
Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.
Highlights
Precision agriculture is a farming management concept based on monitoring, measuring, and responding to variability in crops
The work presented in this paper aims to include the machine vision techniques in agriculture-related tasks focusing on crop farming
The experimental results showed that the proposed approach outperforms K-means clustering and Otsu segmentation on images captured under real field conditions
Summary
Precision agriculture is a farming management concept based on monitoring, measuring, and responding to variability in crops. The research on precision agriculture aims to define a decision support system for farm management by optimizing the returns on input while preserving resources. Machine vision has been widely used to support precision agriculture by providing automated solutions to tasks that are traditionally performed manually. Manual methods tend to be tedious and error prone. Machine vision can provide accurate and efficient solutions to support agricultural activities. Machine learning algorithms enable the analysis of massive volumes of data quickly and accurately, providing a means for the implementation of machine vision applications in agriculture
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