Remotely Sensed Crop Disease Monitoring by Machine Learning Algorithms: A Review

Abstract

Crop pests and diseases are treated as one of the main factors affecting food production and security. An accurate detection and corresponding precision management to reduce the spread of crop diseases in time and space is an important scientific issue in crop disease control tasks. On the one hand, the development of remote sensing technology provides higher-quality data (high spectral/spatial resolution) for crop disease monitoring. On the other hand, deep learning/machine learning algorithms also provide novel insights for crop disease detection. In this paper, a comprehensive review was conducted to demonstrate various remote sensing platforms (e.g. ground-based, low-attitude and spaceborne scales) and popular sensors (e.g. RGB, multispectral and hyperspectral sensors). In addition, conventional machine learning and deep learning algorithms applied for crop disease monitoring are also reviewed. In the end, considering the crop disease early detection problem which is a challenging problem in this area, self-supervised learning is introduced to motivate future research. It is envisaged that this paper has concluded the recent crop disease monitoring algorithms and provides a novel thought on crop disease early monitoring.