For potato external defect detection, ordinary spectral technology has limitations in detail detection and processing accuracy, while the machine vision method has the limitation of a long feedback time. To realize accurate and rapid external defect detection for red-skin potatoes, a non-destructive detection method using hyperspectral imaging and a machine learning model was explored in this study. Firstly, Savitzky–Golay (SG), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), the normalization algorithm, and different preprocessing algorithms combined with SG were used to preprocess the hyperspectral data. Then, principal component regression (PCR), support vector machine (SVM), partial least squares regression (PLSR), and least squares support vector machine (LSSVM) algorithms were used to establish quantitative models to find the most suitable preprocessing algorithm. The successive projections algorithm (SPA) was used to obtain various characteristic wavelengths. Finally, the qualitative models were established to detect the external defects of potatoes using the machine learning algorithms of backpropagation neural network (BPNN), k-nearest neighbors (KNN), classification and regression tree (CART), and linear discriminant analysis (LDA). The experimental results showed that the SG–SNV fusion hyperspectral data preprocessing algorithm and the KNN machine learning model were the most suitable for the detection of external defects in red-skin potatoes. Moreover, multiple external defects can be detected without multiple models. For healthy potatoes, black/green-skin potatoes, and scab/mechanical-damage/broken-skin potatoes, the detection accuracy was 93%,93%, and 83%, which basically meets the production requirements. However, enhancing the prediction accuracy of the scab/mechanical-damage/broken-skin potatoes is still a challenge. The results also demonstrated the feasibility of using hyperspectral imaging technology and machine learning technology to detect potato external defects and provided new insights for potato external defect detection.

Design and experiment of a Chinese chive harvester

In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. In this study, fluorescence spectrum acquisition and hyperspectral imaging processing of tomato leaf samples with and without pesticides were conducted, and spectral data from regions of interest of hyperspectral images were extracted. The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. According to the results, R² of the model of hyperspectral data after SG-SNV preprocessing and PLSR modeling reached 0.9974, RMSEC reached 0.0221, and RMSEP reached 0.0565. R² of the model of fluorescence spectral data after SG-MSC preprocessing and SVM modeling reached 0.9986, RMSEC reached 0.2496, and RMSEP reached 0.4193. Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. Therefore, the fluorescence spectral and hyperspectral imaging techniques applied to tomato leaf pesticide detection enjoy a promising application prospect.

In view of the problems of low work efficiency and high operating costs caused by manual harvesting of Chinese cabbage in China, in this study, a Chinese cabbage harvester with agronomic integrity was designed. The harvester is mainly composed of a crawler chassis, a drawing device, a flexible clamping device, a cutting device, and a horizontal delivery device. Firstly, physical properties of Chinese cabbage such as diameter, plant height, weight, and drawing rate of Chinese cabbage were measured and analyzed to provide necessary basic data for the design of the harvester. Secondly, simulation tests were conducted on the Chinese cabbage harvesting process; a 3D model of Chinese cabbage using SolidWorks 2022 was established and filled with particles using the three-layer stacking method. At the same time, SolidWorks was applied to simplify the model of the Chinese cabbage harvester. The belt of the machine model was set as a flexible body through RecurDyn 2023 software and coupled with EDEM 2022 for simulation analysis. Based on single factor tests, the BBD model was applied to conduct multi-factor response surface analysis on the above factor levels. The optimal working conditions of the harvester were obtained as follows: the rotating speed of the cutting device was 207.85 r/min, the rotating speed of the flexible clamping conveyor belt was 165.51 r/min, the rotating speed of the drawing device was 102.38 r/min, and the machine walking speed was 1.37 km/h. The qualified rate of Chinese cabbage harvesting was the highest, achieving a maximum theoretical value of 97.91%. Field validation tests were conducted on the designed Chinese cabbage harvester. Based on the actual operating conditions of the Chinese cabbage harvester and the simulated operating parameters, the optimal parameter combination was finally determined as follows: rotating speed of the root cutting device was 200 r/min, rotating speed of the flexible clamping conveyor belt was 160 r/min, rotating speed of the drawing device was 100 r/min, and machine walking speed was 1.4 km/h, respectively. Through field verification tests, the highest qualified rate of Chinese cabbage harvesting reached 93.19%, showing a good harvesting effect, which approximates the simulated optimal qualified rate of 97.91%, meeting the mechanized harvesting demand of Chinese cabbage. This study provides reference to the further design and development of Chinese cabbage harvesters in the future.