Rapid And Non-destructive Detection Of Diseases On Tomato Plants Using Hyperspectral Imaging Technology

With a near-20-year development,remote sensing hyperspectral imaging technology has become a research hotspot in the realization of sustainable agricultural development.Moreover,it has received widespread attention and also faces many challenges.In order to better guide agricultural production practices in the future,it is necessary to develop and improve the near-surface spectral research at this stage.The purpose of this research is to take tomato,one of the important consumer vegetables,as the research object,and use hyperspectral imaging technology as the detection method to establish the detection model of tomato leaf mold from the two angles of spectrum and image.Determine the optimal solution based on the evaluation index.Then,the whole process also cross checks the Acanthopanax senticosus black spot disease.The main work of this paper is as follows:(1)Collect a large number of hyperspectral images of tomato leaves and mark healthy and diseased areas.Discuss the feasibility of using spectral index to monitor tomato leaf mold in the two wavelength ranges of visible light-near infrared(400-1000nm)and short-wave near infrared(900-1700nm)respectively.Three types of spectral processing methods: Derivative Spectral Intensity,Envelope Removal and Binary Coding are used to extract characteristic wavelengths/bands and compared with the original spectral data to evaluate the classification results.(2)Using ENVI software to build a spectral database,it mainly completes the visualization processing after modeling using spectral characteristics in the visible-near-infrared band,to achieve precise positioning of tomato leaf mold.Comprehensive three steps of spectral data preprocessing,spectral intensity feature extraction and spectral classification modeling,match the image classification results with various regions of the original image,and evaluate the classification effect.The results show that the establishment and improvement of the spectral library can effectively promote the development of hyperspectral technology in agricultural production;The algorithmic of ICA,PCA and MNF is selected to complete dimensionality reduction and extract spectral characteristic parameters,the last method not only improves computing efficiency but also retains more effective information;This article mainly discusses all the classification results obtained by using different kernel function modeling of support vector machines are better.Among them,the Sigmoid kernel function is effective in detecting tomato leaf mold.(3)Finally,the above method was used to cross-validate the black spot detection of Acanthopanax senticosus leaves.Specific spectral modeling schemes have different recognition effects for different plant diseases.Experiments have proved that it is better to use polynomial kernel function to identify such diseases.The above research results can realize the detection of leaf diseases during tomato growth,provide an experimental basis for precise positioning of plant leaf diseases.In addition,it can also extend the research to various aspects such as nutrients and pesticide residues.