•Low-cost RGB-D sensor (Kinect V2) was employed to estimate diameter of grafted apple tree.•Deep learning contributed to automatically locate grafting position.•SOLOv2 model for segmenting grafting ...position achieved AP of 0.811 and AR of 0.830.•Appropriate distance thresholds in X, Y, and Z directions were determined to estimate diameter.
Apple tree phenotyping can reflect individual development of single apple tree, which mainly involves tree height, crown width, and diameter of apple tree trunk (DATT). This study aimed to estimate diameter of grafted apple tree trunk, whose target position of diameter estimation is about 10 cm above grafting position. An estimated DATT approach of combining red–greenblue-depth (RGB-D) sensor with SOLOv2 was proposed. Firstly, Kinect V2 was employed to obtain original RGB images and point clouds of the grafted apple trees simultaneously. There were 120 and 60 RGB images and corresponding point clouds randomly collected from two modern apple orchards. Secondly, SOLOv2 deep learning model was selected and trained to instance segment grafting position from RGB image for determining it automatically. Then, corresponding exact position of the grafting position in point cloud was mapped by coordinate transformation of its pixel coordinates, which was obtained by trained SOLOv2 model. Finally, DATT was estimated by calculating the difference between maximum and minimum Y coordinates of points selected by distance thresholds in X, Y, and Z directions near the target position, which were 0.10 m, 0.035 m, and 0.20 m, respectively. Results showed that average precision and average recall of the trained SOLOv2 model for instant segmenting the grafting position were 0.811 and 0.830, respectively. Mean absolute error, mean absolute percentage error, and root mean square error of the proposed method were 3.01 mm, 5.86%, and 3.79 mm, respectively. It illustrates that the proposed method can estimate DATT and thus contribute to automatic apple tree phenotyping.
In this study, the fungal strain KNUF-21-F39 was isolated from a declined apple tree (Malus domestica) in the Chungcheongbuk province in Korea. The strain KNUF-21-F39 presented a slow growth rate and ...a variety of macroconidia shapes and sizes ranging from ovoid to fusoid and 1- to 5-septate, primarily showing 3- and 4-septate, with “S”-shaped macroconidia rarely observed. The strain was identified based on morphological characteristics along with phylogenetic analysis performed using the internal transcribed spacer region (ITS) and partial sequences of translation elongation factor 1-α (tef1), RNA polymerase largest subunit (rpb1), and calmodulin (cal) genes. The fungal strain KNUF-21-F39 was identified as Fusarium diversisporum, which has not been previously reported in Korea. The ice nucleation activity (INA) of the strain was also evaluated, identifying the strain as positive for INA. This is the first report characterizing F. diversisporum as an IN-active fungal species.
•Apple tree wood residues were evaluated for the extraction of polyphenols.•Efficiency of conventional and microwave-assisted extraction were compared.•Microwave root extracts have higher phenolic, ...flavonoid and antioxidant activity.•Phloridzin was the main contributor to the phenolic profile of apple tree residues.
For the first time, the characterization of antioxidant activity and phenolic profile of apple tree (Malus domestica) bark, core and roots was carried out. Phenolic compounds were extracted from the Belgium apple tree wood residues collected at two seasons, namely summer 2015 and winter 2016, using conventional (CE) and microwave-assisted extraction (MAE) techniques. For each extraction technique, the influence of the most important operational parameters, namely solvent composition, extraction time and temperature, on the total phenolic and flavonoid content, and antioxidant activity by the 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (DPPH-RSA) and ferric reducing activity power (FRAP) assays were optimized. The phenolic profile from the obtained extracts was also characterized by high-performance liquid chromatography with photodiode array detection (HPLC-PDA). Optimum conditions were: 20mL ethanol:water 60:40v/v, 20min, 100°C, sample weight 0.1g for MAE and 20mL ethanol:water 50:50v/v, 2h, 55°C, sample weight 0.5g for CE. Root extracts obtained by MAE (the most efficient technique) presented the highest phenolic (47.7±0.9mg gallic acid equivalents/g dry weight) and flavonoid (17.1±0.8mg epicatechin equivalents/g dry weight) content, and antioxidant activity (28.4±2.0mg trolox equivalents/g dry weight and 36.1±2.7mg ascorbic acid equivalents/g dry weight for DPPH-RSA and FRAP assays, respectively), followed by bark and core wood extracts. HPLC-PDA analysis revealed that phloridzin was the main contributor to the phenolic composition representing 52%–87% of the total amount of phenolic compounds quantified, while phenolic acids represents less than 10%. This study reveals the potential of apple tree wood residues valorization through the recovery of phenolic compounds for food, pharmaceutical and cosmetic applications.
Early diagnosis and accurate identification of apple tree leaf diseases (ATLDs) can control the spread of infection, to reduce the use of chemical fertilizers and pesticides, improve the yield and ...quality of apple, and maintain the healthy development of apple cultivars. In order to improve the detection accuracy and efficiency, an early diagnosis method for ATLDs based on deep convolutional neural network (DCNN) is proposed. We first collect the images of apple tree leaves with and without diseases from both laboratories and cultivation fields, and establish dataset containing five common ATLDs and healthy leaves. The DCNN model proposed in this paper for ATLDs recognition combines DenseNet and Xception, using global average pooling instead of fully connected layers. We extract features by the proposed convolutional neural network then use a support vector machine to classify the apple leaf diseases. Including the proposed DCNN, several DCNNs are trained for ATLDs recognition. The proposed network achieves an overall accuracy of 98.82% in identifying the ATLDs, which is higher than Inception-v3, MobileNet, VGG-16, DenseNet-201, Xception, VGG-INCEP. Moreover, the proposed model has the fastest convergence rate, and a relatively small number of parameters and high robustness compared with the mentioned models. This research indicates that the proposed deep learning model provides a better solution for ATLDs control. It could be also integrated into smart apple cultivation systems.
Plant resistance inducers (PRIs) and nitrogen (N) nutrition are both known to affect plant defence but their interaction has not been well described. We addressed this question in apple (Malus ...domestica) by generating a transcriptomic data set of young leaves from seedlings grown in subirrigation systems allowing variations in nitrate supply as the sole nitrogen source. Plants under three contrasting N status (high; limited for 10 days; or just resupplied after a 12 days limitation) received foliar applications of the chemical elicitor acibenzolar-S-methyl (ASM), a functional analog of salicylic acid, or water. Two days later, the youngest developed leaves were sampled for total RNA extraction and sequencing analysis (RNAseq). The current dataset includes 1) a detailed protocol of plant sample production and 2) transcriptomic profile description of young leaves as normalized counts obtained from sequence mapping against the Malus domestica GDDH13v1.1 reference transcriptome. The raw data files and processed data are available at the Gene Expression Omnibus (GEO) repository under the accession number GSE264541. This dataset is a valuable resource to investigate further the molecular mechanisms underlying the role of nitrogen and/or ASM treatment in Malus domestica.
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.
•Explored whether agroforestry is beneficial to the water relationship of plant.•Apple tree and corn compete for water sources at 40−80 cm.•WUE of vegetation in apple-corn combination were higher ...than that in monoculture.•40−80 cm should be sub-irrigated in the compound planting of apple tree and corn.
Agroforestry of fruit tree-crops are widely used in the ecological construction of returning farmland to forestry in the Loess Plateau area, but disagreement persists over the water relationship between fruit tree and crops. To explore the rationality of fruit tree and crop intercropping, the stable isotopes were used to investigate the water sources of apple trees and corn in apple tree monoculture (A), corn monoculture (C) and apple-corn combination (AC), and the WUE of vegetation. The results indicated that the water source of apple tree was not significantly different between A and AC, though the utilization of water sources of corn in C was higher than that of AC. The layers of 60−80 cm (20.9–25.9 %) and 80−100 cm (22.8–24.7 %) were the major water source of the apple tree, while the corn also had two fixed water sources of 20−40 cm (18.8–33.1 %) and 40−60 cm (20.6–33.7 %) during the growth period. In addition, the apple tree mainly absorbed water from 40−60 cm (21.8–24.9 %) in the early and middle growth stages and from 100−200 cm (19.7–21.1 %) the in late growth stages. The corn predominantly used water from 0−20 cm (20.5–26.4 %) in the early growth stages and from 60−80 cm (17.2–42.5 %) in the middle and late growth stages. This indicates that there were competitions for water sources at 40−80 cm between apple tree and corn during the growth season. The water use efficiency (WUE) indicated that compound planting can improve the WUE of apple tree and corn. The WUE of corn in compound planting was 3.03–5.26 % higher than that of monoculture, though the WUE of apple trees in combination was higher than that of monoculture only when the soil water content was low. To achieve better ecological and economic benefits, 40−80 cm should be frequently sub-irrigated in the compound of apple tree and corn.
•Although much research has been conducted on regulated deficit irrigation, there is little research on the regulated deficit irrigation of apple trees under the special irrigation method of ...surge-root irrigation in the Loess Plateau of China.•The water consumption of apple trees is higher than that of ordinary crops, and the water demand during the different growth periods is inflexible.•The main irrigation method employed in this region is flood irrigation, and the water use efficiency is low.•An appropriate irrigation system is urgently required to improve the water use efficiency of apple trees in this region.•This study investigated the response of the fruit quality, fruit yield, and water use efficiency to regulated deficit irrigation during the different growth stages of apple trees (Malus pumila Mill) in the Loess Plateau of northern China, to determine the optimal water deficit period of apple trees to provide a scientific basis for water management and the precise irrigation of apple trees.
This study investigated the response of the fruit quality, fruit yield, and water use efficiency (WUE) to regulated deficit irrigation (RDI) during the different growth stages of apple trees (Malus pumila Mill) in the Loess Plateau of northern China. Different water deficit treatments were applied in 2016 and 2017 on a field planted with 5-year-old apple trees. The treatments included low (L), moderate (M), and severe (S) water deficit treatments during the bud burst to leafing (I), flowering to fruit set (II), and fruit growth (III) stages. Compared with full irrigation (FI), water deficit treatment during the different growth stages had significant effects on the fruit quality, fruit yield, and WUE of the apple trees. The L and M water deficit treatments during stage III significantly reduced the apple yield by 10.89% and 13.46% in 2016 and 3.66% and 10.10% in 2017, respectively. A water deficit during stage III decreased the single fruit weight, excellent-fruit percentage, and fruit water content by 2.79%–11.31%, 15.24%–20.36%, and 4.26%–10.07%, respectively, and increased fruit firmness, soluble solid content, and soluble reducing sugar content by 12.70%–21.31%, 13.83%–33.60%, and 10.13%–21.48%, respectively. The L and M water deficit treatments applied during stage I resulted in apple quality and yield that were similar to those resulting from the FI treatment, but the WUE was significantly higher in the L and M water deficit treatments than in the FI treatment. The optimal period for water deficit treatment is stage II, during which the highest yield and WUE were found. The L and M treatments during stage II increased the fruit yield by 13.93% and 13.28% in 2016 and 17.94% and 17.13% in 2017, respectively. The WUE of the apple trees was higher with the I I-L and I I-M treatments (greater than 7 kg m−3) than with other treatments. In addition, water deficit treatment during stage II caused a slight increase in fruit firmness and a slight decrease in fruit water content, which produces apples suitable for storage. Single fruit weight, excellent-fruit percentage, and soluble solid and soluble reducing sugar content were significantly improved, making the apples sweeter; thus, a water deficit during stage II had a significant positive effect on apple quality, with the I I-M treatment being optimal and the II-L treatment being second best. The optimal water deficit treatment of the II-M treatment enhances the fruit quality, yield, and WUE of apple trees in water-scarce environments.
The apple orchards with large land extensions represent a challenge in monitoring crops. Remote sensing techniques can analyze and obtain information on the general state of crops using multispectral ...data and vegetation indexes. This study aimed to analyze the flowering phenological of three apple orchards using images from Sentinel-2 satellite and a Phantom 4 pro/pro+ drone in three apple-producing regions. The images were processed using ArcGIS software and the Normalized Difference Vegetation Index (NDVI). The results were statistically analyzed through discriminant to classify and identify significant differences between the flowering phenological stages. NDVI maps were obtained for the study areas, and the NDVI values ranged from 0.09 to 0.26 and from 0.22 to 0.35 for drone and satellite images, respectively. It was possible to differentiate between two groups of phenological stages in the apple orchards (pre-bloom and post-bloom). The information generated can be a complementary tool for monitoring the apple tree crop.
•In orchards, acquiring the maximum data possible is essential for growers.•Propose a deep learning model to help growers measure and acquire crop data.•Construct a large apple tree detection, ...segmentation, and measurement dataset.•The proposed model achieved a high count accuracy of 94.1% and a high segmentation accuracy of 97.1%.•The average measurement accuracy for the proposed model is over 92%.
Manual measurement and visual inspection is a common practice for acquiring crop data in orchards and is a labor-intensive, time-consuming, and costly task. Accurate and rapid acquisition of crop data is vital for monitoring the dynamics of tree growth and optimizing farm management. In this work, we present a technique for orchard data acquisition and analysis that uses remote imagery acquired from unmanned aerial vehicles (UAVs) combined with deep learning convolutional neural networks to automatically detect and segment individual trees and measure the crown width, perimeter, and crown projection area of apple trees. By using an UAV platform, 50 high-resolution images of apple trees were collected from an orchard during dormancy (bare branches), and then each apple tree was detected by using a Faster R-CNN object detector. Based on these results, each tree was segmented by using a U-Net deep learning network. After convex tree boundaries were extracted from the semantic segmentation results by using an efficient pruning strategy, the crown parameters were automatically calculated, and the accuracy was compared with that obtained by manual delineation. The results show that the proposed remote sensing technique can be used to detect and count apple trees with precision and recall of 91.1% and 94.1%, respectively, segment their branches with an overall accuracy of 97.1%, and estimate crown parameter with an overall accuracy exceeding 92%. We conclude that this method not only saves labor by avoiding field measurements but also allows growers to dynamically monitor the growth of orchard trees.
PDF
