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.

Citation

E. Mavridou, E. Vrochidou, G. A. Papakostas, T. Pachidis and V. G. Kaburlasos, “Machine vision Systems in Precision Agriculture for Crop Farming”, Journal of Imaging, DOI: 10.3390/jimaging5120089, 2019

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