The detection of banana fruits is an important part of intelligent management in the banana plantation. To detect the banana fruit quickly and accurately in the complex orchard environment, this article proposes a method based on the latest deep learning algorithm to detect the banana fruit. Using a monocular camera, we applied the YOLOv4 neural network algorithm to extract the deep features of banana fruits, realizing accurate detection of different banana sizes. The detection algorithm achieved a 99.29% detection rate, the average execution time was 0.171s, the shortest execution time was 0.135s, and the AP was 0.9995. Moreover, the detection results were discussed with the YOLOv3 algorithm and the machine learning algorithm. Compared with the machine learning algorithm, deep learning algorithm was superior to both detection accuracy and detection time. YOLOv4 had higher detection confidence and higher detection rate than YOLOv3. The results show that the proposed method could realize the fast detection of different varieties and different maturity in banana plantations, under different illumination and occlusion conditions, and provide information for banana picking, maturity and yield estimation.
At present, there are few reports on the profiling mechanism that can achieve surface envelope profiling along the surface of a shaft whose radius is constantly changing. Existing profiling mechanisms cannot achieve this function. To this end, a novel deployable arc profiling mechanism is presented in this paper. The mechanism can realize centering deployment along the shaft with a changing radius. The radius of the deployable arc can be adapted to the continuous change of shaft radius, and its surface can always maintain the arc shape for surface envelope profiling. The mechanism is mainly composed of compound cams. Each cam contains multiple grooves, and each groove connects to an arc support linkage. The arc support linkage is controlled by the compound motion of cams in different layers. The pitch curve of each groove is designed by applying the method of relative motion and inverse solution and obtained various parameter equations of the mechanism. The feasibility of this mechanism is verified by analysis, experiment, and application test. The results show that the proposed deployable arc profiling mechanism can achieve the design purpose and the profiling accuracy is kept above 96.425%.
Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.
The growing demands for high level of energy efficiency have given much new impetus to the development of electric vehicle in Agricultural Machines. To improve the performance of the state observer for an 8-DOF four-wheel vehicle, a state observer based on the Extended Kalman Filter is proposed. With the observer, the tire-road forces and the vehicle roll velocity are estimated accurately according to the simulation results. With the consideration of the nonlinear characters of tire and electric vehicle, the electronic differential controller is proposed to select the wheel slip ratios as the control tracking variables and distribute the torques to ensure the steering stability by using modified exponent approaching sliding mode control method. The proposed electronic differential controller is robust to improve the handling performance and control response of electric vehicle with the numerical verification.
As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment.
The effective deposition of pesticide droplets on a target leaf surface is critical for decreasing pesticide application rates. The wettability between the target leaf surface and the pesticide spray liquid should be investigated in depth, with the aim of enhancing the adhesion of pesticide solutions. The wetting and deposition behavior of pesticides on target leaves depends on the properties of the liquid and the physical and chemical properties of the leaves. The physical and chemical properties of leaves vary with growth stage. This study aims to investigate the wetting behavior of banana leaf surfaces at different stages.
This study evaluated the effect of shaking amplitude and capturing height on mechanical harvesting of fresh market apples in trellis trained trees. A linear-forced limb shaker with adjustable shaking amplitude and frequency was designed and fabricated. Shaking amplitudes of 20, 25, 30, 35 and 40 mm were accessible. A catcher filled with 50 mm thickness of peanut foam underneath a piece of cotton was developed. The shaker and the catcher mounted on a movable lifting platform were integrated into a shake-and-catch harvesting system. The approximate middle of a targeted limb was selected as the shaking point and detached fruits were captured underneath the targeted section. The overall combinations of five levels of shaking amplitude and two levels of capturing height were tested for 'Pink Lady' apple trees trellis trained in a vertical fruiting wall architecture. Shaking frequency with 20 Hz and duration with 5 s were used in all tests. Fruit removal efficiency and fruit quality (USDA standard) were adopted to evaluate the quality of the harvesting system. Statistical analysis shows that fruit removal efficiency was significantly improved with increase of amplitude at a certain range; the capturing height significantly affected the percentage of Extra Fancy grade fruit. The results indicated that shaking amplitude with ~30 mm is sufficient to remove majority of fruits in the tested variety; capturing fruits that are much closer to the targeted limb is promising to obtain more Extra Fancy grade fruit.
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