Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. Results We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. Conclusions Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway.

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Early apple bruises, especially those occurring within half an hour, usually have no external symptoms and are di cult to nd. In this study, a fast and nondestructive detection method for early bruises based on a near-infrared camera and image recognition was developed. A total of thirty apple samples were photographed on both sides of each apple. Grayscale images of the apples were captured using a nearinfrared camera with a wavelength region between 900 and 2350 nm. Images of apples (n = 62) without bruises were collected. The same apples were arti cially damaged and photographed by the near-infrared camera immediately. The apples were photographed again at 30-35 min after bruising, and a total of 186 grayscale images were collected. As the glossiness of apples limits the accuracy in the detection of defects, a compound method was proposed consisting of nonlinear grayscale transformation and frequency-domain image ltering techniques, followed by the rst derivative to obtain the gradient grayscale image. Since bruises had distinct edges, bruise edge pixels were detected instead of sound bruise pixels. The compound method obtained a 97.62% classi cation accuracy for nonbruised apples and apples with fresh bruises. The experimental results show that it is feasible to identify early bruises in apples based on near-infrared camera imaging and gradient grayscale images. The method can also provide a reference for the in-situ nondestructive early bruise detection of apples and other fruits.

Sorghum has a long history of cultivation and is an important food and economic crop. It can be divided into glutinous and non-glutinous varieties according to the starch structure and content. Rapid discrimination between the two would help the winemaking, feed, and food industries complete purchase pricing, ingredients, and quality control. In this study, 38 different samples were acquired, including 14 glutinous and 24 non-glutinous sorghum samples. Near infrared (NIR) spectra of glutinous and non-glutinous sorghum, pre-treated using the standard normal variable (SNV) transformation were found to have slightly different absorbances in the combination and first overtone bands. Based on the distribution of the starch-related and hydrogen-containing groups in the NIR region, it was concluded that glutinous sorghum has more C-O and C-C groups than non-glutinous sorghum. This study proposes an approach based on typical samples and direct calibration (TSDC) for binary discrimination. The TSDC approach consists of three functions. First, typical samples of two types of samples were selected. Second, typical type samples are used as dependent variables, predicted samples are used as independent variables, and formula regression is used to obtain fitted coefficients. Finally, if the formula regression model has no solution or the fitted coefficient is 1, typical type samples are reselected. Using the TSDC approach, discrimination accuracy can achieve 100% accuracy at 0.5 threshold. A larger threshold can be set to select better type characteristic predicted samples for discrimination. The TSDC approach can build excellent model through real relevance between the NIR spectra and the properties of interest, and the use of typical type samples greatly reduces modeling work compared with complex pattern recognition methods, especially for highly varied agricultural products. Therefore, it can efficiently propel the application and development of NIR detection technology. More research is required to apply the TSDC approach to three types of samples.

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