The harvesting time of fresh tea leaves has a significant impact on product yield and quality. The aim of this study was to propose a method for real-time monitoring of the optimum harvesting time for picking fresh tea leaves based on machine vision. Firstly, the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm, filtering with a median filter algorithm, binary processing with the Otsu algorithm, and noise reduction and edge smoothing using open and close operations. Then the leaf characteristics, such as leaf area index, average length, and leaf identification index, were calculated. Based on these, the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status. When this method was applied to a RGB tea-tree canopy image acquired at 45° shooting angle, the fresh tea-leaf recognition rate was 90.3%, and the accuracy for fresh tea-leaf harvesting status was 98% by cross validation. Hence, this method provides the basic conditions for future tea-plantation operation and management using information technology, automation, and intelligent systems.
In plant protection, the increasing maturity of unmanned aerial vehicle (UAV) technology has greatly increased efficiency. UAVs can adapt to multiple terrains and do not require specific take-off platforms. They do well, especially in farmland areas with rugged terrain. However, due to the complex flow field at the bottom of a UAV, some of the droplets will not reach the surface of a plant, which causes pesticide waste and environmental pollution. Droplet deposition is a good indicator of the utilization rate of pesticides; therefore, this review describes recent studies on droplet deposition for further method improvement. First, this review introduces the flight altitude, speed, and environmental factors that affect pesticide utilization efficiency and then summarizes methods to improve pesticide utilization efficiency from three aspects: nozzles, electrostatic sprays, and variable spray systems. We also point out the possible direction of algorithm development for a UAV’s precision spray.
Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume.
In order to provide the basis for the design and control of a citrus picking robot clamp and cutter, and find the optimal combination of cutting parameters, this study used a self-made citrus stem cutting test-bed to study the effects of the citrus stem diameter, blade cutting speed, blade cutting clearance, and tool sliding cutting angle on the peak cutting force of citrus stems through a single-factor experiment. Based on the single-factor test, the blade cutting speed, blade cutting clearance, and tool sliding angle are selected as the influencing factors, and a multi-factor test is carried out with the target of the peak cutting force, and the regression model is established. The results showed that the peak cutting force increased linearly with the diameter of the fruit stalk, decreased with the increase of the blade cutting speed and sliding cutting angle, and first decreased and then increased with the increase of the blade cutting clearance. Through the optimisation analysis of the regression model, it is found that the optimal cutting parameter combination is the blade cutting speed of 40 mm/min, blade cutting gap of 1 mm, tool sliding cutting angle of 20°, and the peak cutting force under this combination is 168.23 N. The deviation between the predicted value of the peak cutting force and the measured value is less than 2%, and the optimisation result of the cutting parameters is reliable. This study provides a theoretical basis for the optimal design and control of the clamping and cutting mechanism of the citrus picking robot.
Fast assessment of the initial carbon to nitrogen ratio (C/N) of organic fraction of municipal solid waste (OFMSW) is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process. In this study, a novel approach was proposed to estimate the C/N of OFMSW, where an instance segmentation model was applied to predict the masks for the waste images. Then, by combining the instance segmentation model with the depth-camera-based volume calculation algorithm, the volumes occupied by each type of waste were obtained, therefore the C/N could be estimated based on the properties of each type of waste. First, an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks (Mask R-CNN) model. Second, a volume measurement algorithm was proposed, where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property. Then the calculated volume was corrected with linear regression models. The results showed that the trained instance segmentation model performed well with average precision scores AP 50 = 82.9, AP 75 = 72.5, and mask intersection over unit (Mask IoU) = 45.1. A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R 2 =0.97 and root mean square error RMSE = 0.10. The relative average error was 0.42% and the maximum error was only 1.71%, which indicated this approach has potential for practical applications.
Here, the improved multi-scale YOLO algorithm (Improved-YOLOv3) is presented, which was proposed to realize fast and accurate recognition of citrus fruit in a field environment. With the modification of the YOLO-styled network model, a darknet-53 backbone network with residual modules was designed. A multi-scale detection module was to construct a network model for rapid recognition of citrus fruit in complex environments. Using the improved model to detect and identify citrus fruit targets, the network model can extract more feature information. The improved YOLOv3 model was tested with citrus data, and the detection performance of the improved network and the influence of the number of backbone network layers on the feature extraction capability were compared. The results showed a good detection ability (detection rate, accuracy, map, detection speed) for the target fruit, and the improved YOLOv3 network showed higher accuracy. Moreover, the performance of different training models were compared: the Improved-YOLOv3 has stronger robustness, higher detection accuracy, and shorter training time, and can recognize citrus in complex field environments. The experiment showed that the precision of Improved-YOLOv3 was 90.5% and the accuracy reached 94.3%, the recall rate was 90.3%, the detection time was 9.89 ms per frame, which could provide technical support for this visual recognition system of citrus picking robot.
To solve the problem of high labour costs in the strawberry picking process, the approach of a strawberry picking robot to identify and find strawberries is suggested in this study. First, 1000 images including mature, immature, single, multiple, and occluded strawberries were collected, and a two-stage detection Mask R-CNN instance segmentation network and a one-stage detection YOLOv3 target detection network were used to train a strawberry identification model which classified strawberries into two categories: mature and immature. The accuracy ratings for YOLOv3 and Mask R-CNN were 93.4% and 94.5%, respectively. Second, the ZED stereo camera, triangulation, and a neural network were used to locate the strawberry in three dimensions. YOLOv3 identification accuracy was 3.1 mm, compared to Mask R-CNN of 3.9 mm. The strawberry detection and positioning method proposed in this study may effectively be used to supply the picking robot with a precise location of the ripe strawberry.
In order to improve the deposition and uniformity of the pesticide sprayed by the agricultural spraying drone, this study designed a novel spraying system, combining air-assisted spraying system with electrostatic technology. First, an air-assisted electrostatic centrifugal spray system was designed for agricultural spraying drones, including a shell, a diversion shell, and an electrostatic ring. Then, experiments were conducted to optimize the setting of the main parameters that affect the charge-to-mass ratio, and outdoor spraying experiments were carried out on the spraying effect of the air-assisted electrostatic centrifugal spray system. The results showed the optimum parameters were that the centrifugal rotation speed was 10 000 r/min, the spray pressure was 0.3 MPa, the fan rotation speed was 14 000 r/min, and the electrostatic generator voltage was 9 kV; The optimum charge-to-mass ratio of the spray system was 2.59 mC/kg. The average deposition density of droplets on the collecting platform was 366.1 particles/cm 2 on the upper layer, 345.1 particles/cm 2 on the middle layer, and 322.5 particles/cm 2 on the lower layer. Compared to the results of uncharged droplets on the upper, middle, and lower layers, the average deposition density was increased by 34.9%, 30.4%, and 30.2%, respectively, and the uniformity of the distribution of the droplets at different collection points was better.
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