Fast And Nondestructive Detection Of Brassica Napus Growth Information Using Spectral And Multi-Spectral Imaging Technology

Digital agriculture and internet of things (IoT) in agriculture are the most frontier technologies in modern agriculture, and they are also the key and kernel technologies for the development of modern agriculture and the realization of sustainable agriculture. Digital agriculture and IoT in agriculture require the fast, real time, accurate and positional plant growth information acquisition technologies. Obviously, the traditional lab measurements and information acquisition methods cannot meet the demands of modern agricultural development. Therefore, the study on nondestructive detection methods for plant growth information and development of information detection sensors or instruments are the key problems to be dealt of modern agriculture. This study is mainly focused on the Brassica napus L., which is a widely planted, high economic valued and alternative energy resource plant. Based on a four-year experimental study, the SPAD value detection model for oilseed rape leave and canopy were developed for different growing stage, including seedling, blooming, podding stage. The physiological information (acetolactate synthase (ALS) activity, protein content and amino acids) in oilseed rape leaves were firstly measured in a fast and nondestructive way under herbicide propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273) stress. A new method and system were also developed for plant disease (Sclerotinia) early diagnosis of oilseed rape leaves. These results were helpful for the precision management and operation of oilseed rape planting and were also meaningful for the improvement of of rapeseed yield and quality. The main creative results were achieved as follows:(1) A spectral analysis technical method was proposed as spectral preprocessing-effective wavelength selection-linear and nonlinear calibration model. The fast, high precision and nondestructive models were developed for SPAD value detection in oilseed rape leave and canopy during different growing stage, including seedling, blooming, podding stage. A complete comparison was performed among raw spectra and different spectral preprocessing methods. Successive projections algorithm (SPA) was applied for effective wavelengths (EWs) selection. Linear multiple linear regression (MLR) and partial least squares (PLS), and nonlinear least squares-support vector machine (LS-SVM) models were developed for the detection of fresh leaf SPAD value. The regression coefficients (Rp) were 0.7149,0.9431,0.9215 and 0.8557 for seedling, blooming, podding and seedling-blooming-podding-stage, respectively. An exploration research was proceeded to utilize multi-channel (15 channels) spectroscopy for the SPAD detection of oilseed rape canopy, and the best LS-SVM model achieved Rp=0.7122.(2) Vis/NIR spectroscopy was applied for the quantitative relationship between spectral information and physiological information in oilseed rape leaves, and this study firstly realized the fast detection of ALS under herbicide (ZJ0273) stress. The correlation coefficients were 0.9026,0.9179 and 0.9379 for visible region (400-2500 nm), near infrared region (781-2500 nm) and Vis/NIR region (400-2500 nm) models, respectively. In near infrared region (1100-2500 nm),10 EWs selected by regression coefficient (RC) were applied for ALS detection, and the best prediction results was Rp=0.9395.(3) The principles for effective variable selection were proposed, inclueding successive projections algorithm (SPA), regression coefficients (RC), x-loading weights (x-LW) and independent component analysis (ICA). Fast detection methods and models were developed for protein content detection under herbicide (ZJ0273) stress. In visible/near infrared region (400-2500 nm), the optimal results Rp for soluble protein content, unsoluble protein content and total protein content were 0.9351,0.9067, and 0.9338, respectively. In near infrared region (1100-2500 nm), the best model for soluble protein content using dried leaf spectra was SPA-LS-SVM (Raw) with Rp=0.9887.(4) The optimal combination order for direct orghogonal signal correction (DOSC) and SPA were obtained with DOSC-SPA. Fast and nondestructive models were developed for 17 amino acids and total amino acid (TAA) in oilseed rape leaves. The functional mechanism and response rules were descript for TAA under herbicide stress. The result indicated that the Rp by optimal model were over 0.95 for all 17 amino acids. The selected effective wavelength by DOSC-SPA were applied for direct function development for TAA detection, and the results indicated that power function obtained the best prediction results with Rp=0.9968.(5) The early diagnosis models were developed for Sclerotinia in oilseed rape using spectral and multi-spectral imaging technology, including spectral diagnosis model, multi-spectral imaging reflectance diagnosis model and multi-spectral imaging texture feature diagnosis model. An early diagnosis system was also developed for Sclerotinia in oilseed rape. An early diagnosis model for Sclerotinia in oilseed rape leaves using combinational-simulated wavelengths by DOSC-SPA was developed with a good discrimination ratio of 100%. A multi-spectral imaging discrimination model with a discrimination ratio of 100% was developed based on vegetation index (normalized difference vegetation index, Green normalized difference vegetation index and ratio vegetation index) and texture features (moment of inertia, homogeneity, second derivative angular moment, correlation and entropy). The results were quite helpful for practical applications.The above results realized the fast and high precision detection of oilseed rape for whole growing stage and whole growth information. They also supplied theoretical basis of detection instruments and sensors for the determination of oilseed rape nutritional information, physiological information and ecological information, which had a promising application prospect.