Abstract:The objective of this study was to study the structure and physicochemical properties of biochar derived from apple tree branches (ATBs), whose valorization is crucial for the sustainable development of the apple industry. ATBs were collected from apple orchards located on the Weibei upland of the Loess Plateau and pyrolyzed at 300, 400, 500 and 600 • C (BC300, BC400, BC500 and BC600), respectively. Different analytical techniques were used for the characterization of the different biochars. In particular, proximate and element analyses were performed. Furthermore, the morphological, and textural properties were investigated using scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, Boehm titration and nitrogen manometry. In addition, the thermal stability of biochars was also studied by thermogravimetric analysis. The results indicated that the increasing temperature increased the content of fixed carbon (C), the C content and inorganic minerals (K, P, Fe, Zn, Ca, Mg), while the yield, the content of volatile matter (VM), O and H, cation exchange capacity, and the ratios of O/C and H/C decreased. Comparison between the different samples show that highest pH and ash content were observed in BC500. The number of acidic functional groups decreased as a function of pyrolysis temperature, especially for the carboxylic functional groups. In contrast, a reverse trend was found for the basic functional groups. At a higher temperature, the brunauer-emmett-teller (BET) surface area and pore volume are higher mostly due to the increase of the micropore surface area and micropore volume. In addition, the thermal stability of biochars also increased with the increasing temperature. Hence, pyrolysis temperature has a strong effect on biochar properties, and therefore biochars can be produced by changing pyrolysis temperature in order to better meet their applications.

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The aim of this study was to investigate the adsorption of Cu(II) and Zn(II) onto apple tree branches biochar (BC) produced at 300, 400, 500 and 600 °C (BC300, BC400, BC500, and BC600), respectively. The effect of adsorbent dosage, pH value, contact time, initial concentration of Cu(II) or Zn(II), and temperature on the adsorption process were investigated. The result showed that 5 g BC·L−1 was the optimal dosage to remove Cu(II) and Zn(II) from wastewater and the maximum adsorption efficiency was achieved at a pH of 5.0 for all the BCs when the initial concentration of Cu(II) and Zn(II) were 64 and 65 mg L−1, respectively. Adsorption kinetics and isotherm experiments showed that the pseudo-second order equation and the Langmuir isotherm could best describe the adsorption process, indicating that the adsorption of Cu(II) and Zn(II) onto BCs were monolayer processes and chemisorption was the rate limiting step. The values of ΔG0 for the absorption of Cu(II) and Zn(II) on all BCs were negative, while the values of ΔH0 were positive, suggesting that the absorption was a spontaneous endothermic process. The mechanisms of BC adsorption of metal ions adsorption include surface precipitation, ion exchange, and minor contribution by cation-π interaction. BC500 had highest Cu(II) and Zn(II) adsorption capacity under various conditions (except at pH 2.0). The maximum adsorption capacities of Cu(II) and Zn(II) on BC500 were 11.41 and 10.22 mg·g−1, respectively. Therefore, BC derived from apple tree branches produced at 500 °C can be used as an adsorbent to remove Cu(II) and Zn(II) from wastewater.

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

The use of a fast and accurate unmanned aerial vehicle (UAV) digital camera platform to estimate leaf area index (LAI) of kiwifruit orchard is of great significance for growth, yield estimation, and field management. LAI, as an ideal parameter for estimating vegetation growth, plays a significant role in reflecting crop physiological process and ecosystem function. At present, LAI estimation mainly focuses on winter wheat, corn, soybean, and other food crops; in addition, LAI on forest research is also predominant, but there are few studies on the application of orchards such as kiwifruit. Concerning this study, high-resolution UAV images of three growth stages of kiwifruit orchard were acquired from May to July 2021. The extracted significantly correlated spectral and textural parameters were used to construct univariate and multivariate regression models with LAI measured for corresponding growth stages. The optimal model was selected for LAI estimation and mapping by comparing the stepwise regression (SWR) and random forest regression (RFR). Results showed the model combining texture features was superior to that only based on spectral indices for the prediction accuracy of the modeling set, with the R2 of 0.947 and 0.765, RMSE of 0.048 and 0.102, and nRMSE of 7.99% and 16.81%, respectively. Moreover, the RFR model (R2 = 0.972, RMSE = 0.035, nRMSE = 5.80%) exhibited the best accuracy in estimating LAI, followed by the SWR model (R2 = 0.765, RMSE = 0.102, nRMSE = 16.81%) and univariate linear regression model (R2 = 0.736, RMSE = 0.108, nRMSE = 17.84%). It was concluded that the estimation method based on UAV spectral parameters combined with texture features can provide an effective method for kiwifruit growth process monitoring. It is expected to provide scientific guidance and practical methods for the kiwifruit management in the field for low-cost UAV remote sensing technology to realize large area and high-quality monitoring of kiwifruit growth, thus providing a theoretical basis for kiwifruit growth investigation.