Seed priming with potassium nitrate alleviates the high temperature stress by modulating growth and antioxidant potential in carrot seeds and seedlings.

Early season carrot (Daucus carota) production is being practiced in Punjab, Pakistan to meet the market demand but high temperature hampers the seed germination and seedling establishment which cause marked yield reduction. Seed priming with potassium nitrate breaks the seed dormancy and improves the seed germination and seedling growth potential but effects vary among the species and ecological conditions. The mechanism of KNO3 priming in high temperature stress tolerance is poorly understood yet. Thus, present study aimed to evaluate high temperature stress tolerance potential of carrot seeds primed with potassium nitrate and impacts on growth, physiological, and antioxidant defense systems. Carrot seeds of a local cultivar (T-29) were primed with various concentration of KNO3 (T0: unprimed (negative control), T1: hydroprimed (positive control), T2: 50 mM, T3:100mM, T4: 150 mM, T5: 200 mM, T6: 250 mM and T7: 300 mM) for 12 h each in darkness at 20 ± 2℃. Seed priming with 50 mM of KNO3 significantly enhanced the seed germination (36%), seedling growth (28%) with maximum seedling vigor (55%) and also exhibited 16.75% more carrot root biomass under high temperature stress as compared to respective control. Moreover, enzymatic activities including peroxidase, catalase, superoxidase dismutase, total phenolic contents, total antioxidants contents and physiological responses of plants were also improved in response to seed priming under high temperature stress. By increasing the level of KNO3, seed germination, growth and root biomass were reduced. These findings suggest that seed priming with 50 mM of KNO3 can be an effective strategy to improve germination, growth and yield of carrot cultivar (T-29) under high temperature stress in early cropping. This study also proposes that KNO3 may induces the stress memory by heritable modulations in chromosomal structure and methylation and acetylation of histones that may upregulate the hormonal and antioxidant activities to enhance the stress tolerance in plants.

Development of a dual-arm rapid grape-harvesting robot for horizontal trellis cultivation.

It is extremely necessary to achieve the rapid harvesting of table grapes planted with a standard trellis in the grape industry. The design and experimental analysis of a dual-arm high-speed grape-harvesting robot were carried out to address the limitations of low picking efficiency and high grape breakage rate of multijoint robotic arms. Based on the characteristics of the harvesting environment, such as the small gap between grape clusters, standard trellis, and vertical suspension of clusters, the configuration of the dual-arm harvesting robot is reasonably designed and analyzed, and the overall configuration of the machine and the installation position of key components are derived. Robotic arm and camera view analysis of the workspace harvesting process was performed using MATLAB, and it can be concluded that the structural design of this robot meets the grape harvesting requirements with a standard trellis. To improve the harvesting efficiency, some key high-speed harvesting technologies were adopted, such as the harvesting sequence decision based on the "sequential mirroring method" of grape cluster depth information, "one-eye and dual-arm" high-speed visual servo, dual arm action sequence decision, and optimization of the "visual end effector" large tolerance combination in a natural environment. The indoor accuracy experiment shows that when the degree of obscuration of grape clusters by leaves increases, the vision algorithm based on the geometric contours of grape clusters can still meet the demands of harvesting tasks. The motion positioning average errors of the left and right robotic arms were (X: 2.885 mm, Y: 3.972 mm, Z: 2.715 mm) and (X: 2.471 mm, Y: 3.289 mm, Z: 3.775 mm), respectively, and the average dual-arm harvesting time in one grape cluster was 8.45 s. The field performance test verifies that the average harvesting cycle of the robot with both arms reached 9 s/bunch, and the success rate of bunch identification and harvesting success rate reached 88 and 83%, respectively, which were significantly better than those of existing harvesting robots worldwide.

Resistance characteristics of broad-leaf crop canopy in air-assisted spray field and their effects on droplet deposition.

Air-assisted spray technology is widely applied in high-efficiency pesticide applications. The resistance characteristics of the crop canopy reflect its energy dissipation effect on the assisted airflow, connecting the structure of the crop canopy, assisted airflow velocity, and droplet deposition effect. Using a common broad-leaf crop canopy as the research object, the resistance characteristics of the crop canopy in the air-assisted field were investigated in this study by performing theoretical analysis and wind tunnel tests. Further, the feasibility of using the resistance characteristics of the crop canopy was assessed to evaluate its droplet deposition effect. The results showed that under the conditions of different number of leaf layers and initial leaf azimuth angles, the canopy pressure drop experiences a non-linear increasing trend with increasing assisted airflow velocity and that its regression function conforms to the Darcy-Forchheimer function. Moreover, when the initial azimuth angles of single- and multi-layer leaves were 90°-270°, the change rate of the canopy pressure drop with airflow velocity was 7-9 m/s, and there was a critical wind speed. However, with an increasing number of leaf layers in the crop canopy and changes in the initial leaf azimuth angle, the corresponding changes between the maximum canopy pressure drop and resistance coefficient were non-linear. Thus, it is proposed that the resistance characteristics of multi-layer leaves cannot be quantified as the results of the linear superposition of the resistance characteristics of several single-layer leaves-that is, it should be regarded as a whole research object. Combined with the analysis of the influence of the crop canopy resistance on droplet deposition, it is considered that when the crop canopy has multiple leaf layers in the airflow direction, the existing air-assisted spray technology cannot guarantee droplet deposition and canopy penetration simultaneously.

Development and experimental analysis of a small integrated edge navigation sensor based on principle of circular arc array.

Signal, accuracy, and real-time performance of satellite, radar, and machine vision is a subject of concern in various complex agricultural environments. Therefore, the demand for a robust navigation sensor for indoor and vertical agricultural environment remains crucial, and it is a significant subject. In view of this, the relative edge pose detection method based on the ideal target band principle of the lateral center arc array, in this research, a small integrated arc array navigation sensor module based on adaptive detection arc technology, is developed, which costs about $100, autonomous edge navigation position, and attitude detection is realized in facility agriculture environment with continuous structured corridor or roadside features. In this research, a coupling method of reducing the radius of distance sensor arrangement, adjusting the unequal center angle, and increasing the detection distance is used to realize the miniaturization of the arc array arrangement. A semicircular modular rocket was designed to slide and adjust the center angle of the distance sensor, and the longitudinal installation position of the modularized sensor was adjusted by translating the circular arc of the detection; the convenient moving arrangement under different vehicle width and detection arc characteristics is realized. An adaptive construction method of detecting a circular arc based on self-calibrating detection distance of a distance sensor is proposed, which effectively reduces the difficulty of arranging the lateral central circular arc array; the fast construction of lateral detection arc is realized. In addition, in order to improve the accuracy and stability of the pose detection, the Mahalanobis distance algorithm and the standard Kalman filter are used to optimize the estimation of the ranging information and the relative pose of the edge. The experimental results show that the small integrated arc array navigation sensor module can independently construct photoelectric detection arcs with different characteristics to detect the position and attitude of the relative edge. When the road surface is concave and convex, the small integrated arc array navigation sensor module can still maintain the stable position and attitude detection of the relative edge for more than 30 s. In addition, when the walking speed of the autonomous navigation platform is 0.15 m/s to 0.35 m/s, the detection errors of lateral deviation and heading deviation relative to the road edge are less than 40 mm and 4.5°, respectively. The small integrated arc array navigation sensor module is less affected by the change of operating speed, and still has good accuracy and stability. The results show that the modularized edge navigation sensor has the advantages of fast and convenient use, high accuracy, and low cost; it can be applied to autonomous edge navigation control in greenhouse and plant and animal factories.

Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism.

Crop pests are a major agricultural problem worldwide because the severity and extent of their occurrence threaten crop yield. However, traditional pest image segmentation methods are limited, ineffective and time-consuming, which causes difficulty in their promotion and application. Deep learning methods have become the main methods to address the technical challenges related to pest recognition. We propose an improved deep convolution neural network to better recognize crop pests in a real agricultural environment. The proposed network includes parallel attention mechanism module and residual blocks, and it has significant advantages in terms of accuracy and real-time performance compared with other models. Extensive comparative experiment results show that the proposed model achieves up to 98.17% accuracy for crop pest images. Moreover, the proposed method also achieves a better performance on the other public dataset. This study has the potential to be applied in real-world applications and further motivate research on pest recognition.

Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures.

Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

Pichia anomala Induced With Chitosan Triggers Defense Response of Table Grapes Against Post-harvest Blue Mold Disease.

To study the mechanism by which Pichia anomala induced with chitosan (1% w/v) controls blue mold disease in table grapes caused by Penicillium expansum, this study evaluated alterations in three yeast enzymatic activities. The changes in the five primary disease defense-related enzymes and two non-enzyme activities of table grapes were assayed. The results of the study showed that chitosan (1% w/v) significantly increased the yeast ß-1,3-glucanase, catalase (CAT), and malondialdehyde (MDA) activities. Furthermore, P. anomala alone or induced with chitosan (1% w/v) significantly increased the table grapes enzymatic activities of Polyphenol oxidase (PPO), phenylalanine (PAL), peroxidase (POD), and catalase (CAT) compared to the control. The RT-qPCR results also confirmed that the genes of these major disease defense enzymes were up-regulated when the table grapes were treated with P. anomala. The highest results were recorded when the fruit was treated by yeast induced with chitosan (1% w/v). The phenolic compounds, in addition to their nutritional value, can also increase the antimicrobial properties of table grapes. The current experiment determined that the total phenol and flavonoid contents of table grapes showed the highest results for fruits treated by P. anomala induced with chitosan compared with the control. Generally, the increment of these fruit enzymatic and non-enzymatic activities shows improved table grape defense against the pathogenic fungus. The induction of the yeast with chitosan also increases its bio-control efficacy against the pathogen. This study will enable future detailed investigation in the yeast pathogen control mechanisms and the use of yeasts as bio-pesticides.

Trehalose supplementation enhanced the biocontrol efficiency of Sporidiobolus pararoseus Y16 through increased oxidative stress tolerance and altered transcriptome.

BACKGROUND: In the process of biological control, the antagonistic yeasts contend with various stresses that negatively influence yeasts' biocontrol efficiency. In the current study, we investigated the effect of trehalose supplementation on the biocontrol efficiency and oxidative stress tolerance of Sporidiobolus pararoseus Y16. RESULTS: S. pararoseus Y16, an antagonistic yeast cultured in trehalose supplemented medium, exhibited better biocontrol efficiency against Penicillium expansum and Aspergillus tubingensis in table grapes. Trehalose-treated S. pararoseus Y16 cells showed good proliferation efficiency and oxidative stress tolerance than untreated cells. Increased ß-1,3-glucanase, catalase, superoxide dismutase activity, and low protein carbonylation were observed in trehalose-amended S. pararoseus Y16 upon H2 O2 exposure. The RNA sequencing results indicated that trehalose significantly altered the transcriptome of S. pararoseus Y16. The GO, KEGG, and COG annotations revealed that the differentially regulated genes corresponded to the various biological process of the yeast. CONCLUSION: Our findings suggested that trehalose use could enhance the biocontrol efficiency and oxidative stress tolerance of S. pararoseus Y16. Trehalose supplementation altered the transcriptome of S. pararoseus Y16, particularly the genes that correspond to amino acid metabolism, nucleotide metabolism, and protein modification. Thereby the oxidative stress tolerance and biological control efficiency of S. pararoseus Y16 was enhanced by trehalose. © 2021 Society of Chemical Industry.

Investigation on the Cooperative Grasping Capabilities of Human Thumb and Index Finger.

The maximum cooperative grasping mass and diameter of the human thumb and index finger were investigated by 7560 grasp-release trials on various masses of solid cylinders and various sizes of rings. The maximum grasping mass of the participants' thumb-index finger depended on gender, age and the sum of thumb-index finger lengths (P < 0.05), but not on the hand-used and ratio of index finger to thumb length (P > 0.05). The maximum grasping diameter of the participants' thumb-index finger depended on the age, sum of thumb-index finger lengths and ratio of index finger to thumb length (P < 0.05), but not on the gender and hand-used (P > 0.05). There was a non-linear regression model for the dependence of the maximum grasping mass on gender, age and the sum of thumb-index finger lengths and another non-linear regression model for the dependence of the maximum grasping diameter on the age, sum of thumb-index finger lengths and ratio of index finger to thumb length. Two regression models were useful in the optimal size design of robotic hands intending to replicate thumb-index finger grasping ability. This research can help to define not only a reasonable grasp mass and size for a bionic robotic hand, but also the requirements for hand rehabilitation.

Experiments and Analysis of Close-Shot Identification of On-Branch Citrus Fruit with RealSense.

Fruit recognition based on depth information has been a hot topic due to its advantages. However, the present equipment and methods cannot meet the requirements of rapid and reliable recognition and location of fruits in close shot for robot harvesting. To solve this problem, we propose a recognition algorithm for citrus fruit based on RealSense. This method effectively utilizes depth-point cloud data in a close-shot range of 160 mm and different geometric features of the fruit and leaf to recognize fruits with a intersection curve cut by the depth-sphere. Experiments with close-shot recognition of six varieties of fruit under different conditions were carried out. The detection rates of little occlusion and adhesion were from 80⁻100%. However, severe occlusion and adhesion still have a great influence on the overall success rate of on-branch fruits recognition, the rate being 63.8%. The size of the fruit has a more noticeable impact on the success rate of detection. Moreover, due to close-shot near-infrared detection, there was no obvious difference in recognition between bright and dark conditions. The advantages of close-shot limited target detection with RealSense, fast foreground and background removal and the simplicity of the algorithm with high precision may contribute to high real-time vision-servo operations of harvesting robots.