The Comprehensive Review on Detection of Macro Nutrients Deficiency in Plants Based on The Image Processing Technique

Abstract

Images are a significant source of data and information in agricultural technologies. The use of image processing techniques had important implications for the analysis of smart farm. The analytical system using digital image processing would classify the nutrient deficiency symptoms much prior than a human could identify. This will enable the farmers to adopt appropriate corrective action in time. The paper discusses various methods used in the detection of nutrient deficiencies in plants based on visual images. The image processing techniques have several stages to get the best results in nutrient deficiency detection, namely image acquisition, image enhancement, image segmentation, and feature extraction. Based on the analyses, it is proved that the image processing technology can support the development of farming automation to accomplish the advantages of low price, high efficiency, and high accuracy. Through analysis and discussion, the paper proposed a new technique in every phase of image processing for the detection of nutrient deficiency as the basis of the implementation in future research. Consequently, the research will support the growth of agricultural automation equipment and systems in more smart approaches.