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Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development. By implementing a Digital Surface Model (DSM) to imagery obtained using Unmanned Aerial Vehicles (UAV), it is possible to filter canopy information effectively based on height, which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover crops. This paper describes a method based on the DSM to assess canopy growth (CG) as well as missing plants from a kiwifruit orchard on a plant-by-plant scale. The DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion (SfM) algorithm. An adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular surface. Furthermore, a customized algorithm was developed to discriminate single kiwifruit plants automatically, which allowed the estimation of individual canopy cover fractions (fc). By applying differential fc thresholding, four categories of the CG were determined automatically: (i) missing plants; (ii) low vigor; (iii) moderate vigor; and (iv) vigorous. Results were validated by a detailed visual inspection on the ground, which rendered an overall accuracy of 89.5% for the method proposed to assess CG at the plant-by-plant level. Specifically, the accuracy for CG category (i)- (iv) was 94.1%, 85.1%, 86.7%, and 88.0%, respectively. The proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to distinguish by common colour thresholding or edge identification methods. [ABSTRACT FROM AUTHOR]

The eastern foothills of Helan Mountain are a production area of high-quality wine grapes, but the low content of water-soluble calcium in the alkaline soil in this area has become an important limiting factor for the production of high-end wines. In this study, 7-year-old Cabernet Sauvignon grapes grown at Lilan Winery, which is located at the eastern foot of Helan Mountain in Ningxia, China, were used to examine the effect of exogenous calcium supplementation on fruit growth and berry quality. Calcium sugar alcohol was applied as a foliar spray at 1.2 L/hm² (T1), 2.4 L/hm² (T2), 3.6 L/hm² (T3), 4.8 L/hm² (T4), and 6.0 L/hm² (T5) concentrations at the fruit expansion stage, the early stage of grape veraison, the middle stage of grape veraison, and the later stage of grape veraison. Water control was used for comparison. The results showed that foliar calcium supplementation can significantly enhance the photosynthetic characteristics of wine grape leaves, increase the chlorophyll content of leaves, and improve the quality of grape berries. Under the T3 (3.6 L/hm²) concentration of exogenous calcium, the transverse diameter, the longitudinal diameter, and fruit shape index of grapes were the highest, which were increased by 1.71%, 5.33%, and 3.92% compared to the control, respectively. The application of high calcium concentrations (T4 and T5) inhibited the physiological development of grapes. The spike length, hundred-grain weight, yield of wine grapes, sugar-acid ratio, tannin, and total phenols were the highest in the T2 (2.4 L/hm²) treatment. The results of principal component analysis based on multiple indicators showed that foliar application of 2.4-3.6 L/hm² chelated sugar alcohol calcium on the leaf surfaces of grape leaves was the most suitable for improving the physiological characteristics and berry quality of wine grapes. [ABSTRACT FROM AUTHOR]

With the continuous expansion of wine grape planting areas, the mechanization and intelligence of grape harvesting have gradually become the future development trend. In order to guide the picking robot to pick grapes more efficiently in the vineyard, this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic segmentation network for different varieties of grapes in the natural field environments. To this end, the Convolutional Block Attention Module (CBAM) attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability. Meanwhile, the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers (with more contextual information) extracted by the backbone network. The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset, and it was shown that the improved PSPNet model had an Intersection-over-Union (IoU) and Pixel Accuracy (PA) of 87.42% and 95.73%, respectively, implying an improvement of 4.36% and 9.95% over the original PSPNet model. The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+ and U-Net in terms of IoU, PA, computation efficiency and robustness, and showed promising performance. It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments, which provides a certain technical basis for intelligent harvesting by grape picking robots. [ABSTRACT FROM AUTHOR]

Traditional vine variety identification methods usually rely on the sampling of vine leaves followed by physical, physiological, biochemical and molecular measurement, which are destructive, time-consuming, labor-intensive and require experienced grape phenotype analysts. To mitigate these problems, this study aimed to develop an application (App) running on Android client to identify the wine grape automatically and in real-time, which can help the growers to quickly obtain the variety information. Experimental results showed that all Convolutional Neural Network (CNN) classification algorithms could achieve an accuracy of over 94% for twenty-one categories on validation data, which proves the feasibility of using transfer deep learning to identify grape species in field environments. In particular, the classification model with the highest average accuracy was GoogLeNet (99.91%) with a learning rate of 0.001, mini-batch size of 32 and maximum number of epochs in 80. Testing results of the App on Android devices also confirmed these results. [ABSTRACT FROM AUTHOR]

The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts. More recently, machine learning algorithms for pattern recognition have been successfully applied to leaf recognition in other plant species. These new tools make the classification of Chinese medicinal plants easier, more efficient and cost effective. This study showed comparative results between machine learning models obtained from two methods: i) a morpho-colorimetric method and ii) a visible (VIS)/Near Infrared (NIR) spectral analysis from sampled leaves of 20 different Chinese medicinal plants. Specifically, the automated image analysis and VIS/NIR spectral based parameters obtained from leaves were used separately as inputs to construct customized artificial neural network (ANN) models. Results showed that the ANN model developed using the morpho-colorimetric parameters as inputs (Model A) had an accuracy of 98.3% in the classification of leaves for the 20 medicinal plants studied. In the case of the model based on spectral data from leaves (Model B), the ANN model obtained using the averaged VIS/NIR spectra per leaf as inputs showed 92.5% accuracy for the classification of all medicinal plants used. Model A has the advantage of being cost effective, requiring only a normal document scanner as measuring instrument. This method can be adapted for non-destructive assessment of leaves in-situ by using portable wireless scanners. Model B combines the fast, non-destructive advantages of VIS/NIR spectroscopy, which can be used for rapid and non-invasive identification of Chinese medicinal plants and other applications by analyzing specific light spectra overtones from leaves to assess concentration of pigments such as chlorophyll, anthocyanins and others that are related active compounds from the medicinal plants. [ABSTRACT FROM AUTHOR]