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IntroductionCanopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.MethodsTo solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.ResultsThe analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R2 = 0.91076, RMSE = 0.00070; validation set; R2 = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R2 of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.DiscussionThis study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

A full understanding of the growth and distribution of tree roots is conducive to guiding precision irrigation, fertilization, and other agricultural work during agricultural production. Detecting tree roots with a ground-penetrating radar is a repeatable detection method that does no harm to the earth surface and tree roots. In this research, a rapid and accurate automatic detection was conducted on hyperbolic waveforms formed by root targets in B-scan images based on YOLOv5s. Following this, the regions of interest containing target hyperbolas were generated. Three or more coordinate points on the hyperbola were selected according to the three-point fixed circle (TPFC) method to locate the root system and estimate the root diameter. The results show that the accuracy of hyperbola detection using YOLOv5s was 96.7%, the recall rate was 86.6%, and the detection time of a single image was only 13 ms. In the simulation image, the TPFC method was used to locate the root system and estimate the root diameter through three different frequency antennas (500 MHz, 750 MHz, and 1000 MHz). A more accurate result was obtained when the antenna frequency was 1000 MHz, with the average distance error of root system positioning being 3.17 cm, and the slope and R2 of the linear fitting result between the estimated root diameter and the actual one being 1.029 and 0.987, respectively. Verified by the pre-buried root test and wilderness field test, both root localization and root diameter estimation in our research were proved to gain good results and conform to the rules found in simulation experiments. Therefore, we believe that this method can quickly and accurately detect the root system, locate and estimate the root diameter, and provide a new perspective for the non-destructive detection of the root system and the three-dimensional reconstruction of the root system.

Water stress is a significant element impacting photosynthesis, which is one of the major physiological activities governing crop growth and development. In this study, the photosynthetic rate of Brassica chinensis L. var. parachinensis (Bailey) (referred to as Chinese Brassica hereafter) was predicted using the deep learning method. Five sets of Chinese Brassica were created, each with a different water stress gradient. Air temperature (Ta), relative humidity (RH), canopy temperature (Tc), transpiration rate (Tr), photosynthetic rate (Pn), and photosynthetically available radiation (PAR) were measured in different growth stages. The upper limit and lower limit equations were built using the non-water-stress baseline (NWSB) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) methods. The crop water stress index (CWSI) was then calculated using these built equations. The multivariate long short-term memory (MLSTM) model was proposed to predict Pn based on CWSI and other parameters. At the same time, the support vector regression (SVR) method was applied to provide a comparison to the MSLTM model. The results show that water stress had an important effect on the growth of Chinese Brassica. The more serious the water stress, the lower the growth range (GR). The HDBSCAN method had a lower root mean square error (RMSE) in calculating CWSI. Furthermore, the CWSI had a significant effect on predicting Pn. The regression fitting between measured Pn and predicted Pn showed that the determination coefficient (R2) and RMSE were 0.899 and 0.108 μmol·m−2·s−1, respectively. In this study, we successfully developed a method for the reliable prediction of Pn in Chinese Brassica, which can serve as a useful reference for application in water saving.

China is a big producer of citrus all over the world, and citrus is an important industry of mountainous area in South China. It is main economic source for farmers in citrus main producing areas. 90% citrus is planted on hilly mountain, wherein the site conditions are poor, it is difficult to operate mobile spraying machine in such areas, knapsack sprayer backed by farmers or pesticide spraying vehicle pushed by hands are main modes for spraying operation, and such modes have disadvantages of high labor intensity and low efficiency, thereby affecting timeliness and effectiveness of prevention and control of plant diseases and insect pests in citrus orangeries. Pipeline spraying technique is adopted in a few orange orangeries, there is no scientific calculation and planning design in the establishment of facilities, thereby pipeline spraying technique is lack of systematic design method. Therefore, technical parameters of key equipment and facilities in establishment of mountainous region citrus orangery pipeline constant pressure spraying facilities are studied and calculated on the basis of theoretical calculation and field scientific experiments, thereby providing theoretical and practical guidance for establishing standardized pipeline constant pressure spraying equipment and facilities.

Damaged fruit is the most important barrier to success in the mechanical harvesting of fresh market apples using mass harvesting methods. To address fruit quality issues and expedite the commercialization of mechanical harvesting of fresh market apples, an innovative air suspension-based catching (ASC) mechanism was designed and fabricated to collect fruit detached with a mechanical shaking device (tree limb shaker). The objective of this study was to evaluate the shake-and-catch mechanism in mechanical harvesting of apples. Three apple varieties with different physical characteristics (‘Honeycrisp’, ‘Pacific Rose’, and ‘Pink Lady’) were used in the field trials. The air pressure settings for the ASC mechanism played an important role in reducing the risk of apple damage and improving fruit quality. The effect of the shaking frequency of the tree limb shaker on fruit quality was not significant. When the air pressure increased from 0 to 1030 kPa, the fruit quality greatly improved. Keywords: Air pressure, Air suspension-based catching mechanism, Fluidized bed technology, Fresh market apples, Fruit quality, Mass harvesting, Shaking efficiency.

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

The orchard irrigation is susceptible significantly to various environmental factor but the approach to predict water demand of irrigation remains an outstanding challenge up to now. In this paper, a prediction model of irrigation based on GA-BP neural network has been proposed in orchards, which selects three environmental factors including air temperature, soil moisture content and light intensity as the input of back. propagation neural network. In order to overcome BP’s disadvantage of being easily stuck in a local minimum, genetic algorithm is used to optimize the weight and threshold of neural network. The results showed that the GA-BP neural network model can express the nonlinear relationship between the water demand of litchi and the main environmental factors more accurately. The mean absolute percentage error (MAPE) is only 0.0283, and the correlation coefficient of the target and output value is 0.9799. Hence, the model can provide a theoretical basis for the further development of the intelligent irrigation decision system of litchi orchards.