Detection of nutrition deficiencies in plants using proximal images and machine learning: A review

During the last decade, the combination of digital images and machine learning techniques for tackling agricultural problems has been one of the most explored elements of digital farming. In the specific case of proximal images, most efforts have been directed to the detection and classification of plant diseases and crop-damaging pests. Important progress has also been made on the use of close-range images to determine vegetal nutrient status, but because such studies are fewer and more scattered, it is difficult to draw a complete picture on the state of art of this type of research. In this context, a thorough literature search was carried out in order to identify as many relevant investigations on the subject as possible. Every kind of imaging sensor was considered (visible range, multispectral, hyperspectral, chlorophyll fluorescence, etc.), provided that images were captured at close range, thus excluding research using Unmanned Aerial Vehicles (UAVs), airplanes and satellites. A careful analysis of the techniques for detection and classification was carried out and used as basis for an in-depth discussion on the main challenges yet to be overcome. Some directions for future research are also suggested, having as target to increase the practical adoption of this kind of technology.