China is the largest fruit producer and consumer in the world, while most orchards in the south of China are located in hilly areas. The ropeways can be expected for transporting orchards in mountainous areas. But the harsh working environment can often lead to machine failures in recent years. Therefore, it is of great significance to investigate and solve the problems at the initial stage of failures. Generally, the conventional bearing fault diagnosis system includes five links: signal acquisition, feature extraction, state identification, diagnosis analysis. and decision intervention. Deep learning is widely applied for bearing fault detection in recent years. Traditional machine learning also needs to manually extract the fault features at present, depending mainly on the deep professional knowledge. However, it is high demand for the high performance of fault diagnosis, particularly for simple structures and less calculation during feature extraction. This study aims to realize the fault diagnosis of ropeway drive system bearings in the mountain orchards with poor network environment, in order to ensure the safe and stable operation of ropeway in the mountain orchards. A one-dimensional end-to-end lightweight CNN detection, 1D-MRL-CNN was established to directly detect the one-dimensional vibration signals for the mountain ropeways. Specifically, the new model was established using residual structure and depth separable convolution. The depth separable convolution was applied to greatly reduce the parameter and calculation amount of the improved model. In addition, the residual structure was applied to make up for the accuracy loss caused by depth separable convolution. The parameter amount and complexity of the model were reduced significantly while ensuring the detection accuracy. The stem block and BN layer were then introduced to improve the robustness and generalization ability of the new model suitable for the variable load working state of the ropeway. Finally, the hard_swish activation function was also adopted in the model. The channel attention mechanism was added to the last layer of the main module, in order to improve the feature extraction ability of the network model. Two datasets (Paderborn University and Case Western Reserve University) were used to verify the comprehensive performance, stability under variable load and anti-noise interference performance. The Paderborn University dataset showed that the fault classification accuracy of the improved model was 99.43%, which was 0.56, 0.99, and 1.23 percentage points higher than those of the one-dimensional classical CNN, similar optimal network, and the lightweight CNN architecture optimal network. The parameters and floating-point calculations were 83.44 kb and 0.20 M, which were 2.19%, 1.18%, 0.75%, and 0.83% of the one-dimensional classical CNN classification network architectures (such as Resnet18, Resnet34, Resnet50 and VGG16), 6.19% and 30.40% of the same type of networks 1D-Lenet5 and 1D-Inception, while 4.2%, 2.07%, 2.84%, 3.32% and 5.16% of the one-dimensional lightweight CNN architecture MobileNetV1, MobileNetV2, MobileNetV3-Large, ShuffleNetV1 and EfficientNet-1. In addition, the Case Western Reserve University dataset showed that the average accuracy rate of the improved model was 96.70% in six load scenarios, which was 9.1, 4.7, and 10.5 percentage points higher than those of Resnet18, WDCNN, and MobileNetV3-Large, respectively. The average recognition accuracy was 99.14% under four noise conditions, which was 4.74, 1.24, and 5.51 percentage points higher than those of Resnet18, WDCNN and MobileNetV3-Large, respectively. Finally, the fault classification of the improved model under actual working conditions was verified by the established ropeway dataset, where only 2 fault samples out of 1 400 samples were predicted incorrectly. The new network model was suitable for the bearing fault detection of mountain orchard transport ropeway, due to the small parameters, high accuracy and robustness under variable load and noisy working conditions.

Banana fusarium wilt has a significant adverse effect on the banana industry. It is worth noting that fusarium oxysporum f.sp. cubense 4(Foc4) is one of the most devastating and widespread pathogens of banana fusarium wilt in the world. It is a catastrophic threat to the global banana industry. At present, there is still no technology that can cure bananas infected with banana fusarium wilt. Therefore, it is extremely important for banana planting industry to detect banana fusarium wilt in time and do a good job in prevention and control. In order to realize the early accurate detection of banana fusarium wilt race 4, this study proposed a detection method of double probe paper-based sensor based on colloidal gold, which increased the use of signal enhancement probes to increase the binding amount of colloidal gold, thereby reducing the detection limit. Two kinds of colloidal gold particles with different particle sizes were combined with the corresponding detection probes and signal enhancement probes to form a double gold label probe system. The large particle size colloidal gold was used with the detection probe, while the small particle size colloidal gold was paired with the signal enhancement probe. The sample being tested was mixed with this double gold-labeled probe system. The Foc4 target sequence in the sample would be paired with the double gold-labeled probe to form a ' gold-labeled probe-target sequence-T-line probe ' complex, which continued to migrate to the water-absorbing pad. The complex was captured on the test area of the sensor. Within 10 minutes, the test paper would generate a visible target product that could be observed by the naked eye at both the test line and the quality control line areas, thus achieving qualitative detection. For quantitative detection of Foc4, the light intensity of the test strip would be further analyzed using software. The light intensity peak area was obtained and substituted into the standard curve to obtain the concentration of Foc4 detected and realize quantitative detection. Experimental results demonstrate that the detection limit of the dual-probe paper-based sensor is 0.001 nmol/L, which is 100 times of that of the traditional paper-based sensor, and the detection sensitivity is improved. The sensitivity test results demonstrated a strong linear relationship between the concentration of Foc4 and the peak area of the test line light intensity within the concentration range of 0.001-1000.000 nmol/L. This finding suggests that the sensor can be effectively employed for quantitative detection. To assess specificity, a high concentration of non-complementary probe was utilized as the target sequence for detection and compared with the detection results of samples containing Foc4. The experiment revealed the absence of a red band at the test line when the test paper was detected by the non-complementary sequence. Importantly, this observation indicated that the presence of the non-complementary sequence had minimal impact on the detection effect, thus affirming the sensor's excellent specificity. Finally, the paper-based sensor was used to detect Foc4 in banana leaves. The average recovery rate was 77.6%-102.3%, and the relative standard deviation was 7.4%-7.7%, indicating that it can be used for on-site detection of Foc4. The detection technology possesses the characteristics of being low-cost and easy to operate. In comparison to traditional detection methods, it enables timely, rapid and accurately determination of the presence of Foc4. Compared with the existing molecular detection technology, the detection can be completed without the need of expensive equipment or specialized personnel. This feature makes it highly suitable for widespread use. The dual-probe paper-based sensor realizes the combination of nucleic acid lateral chromatography detection technology and banana fusarium wilt detection. Its potential for widespread adoption in the early disease warning system of the banana industry is substantial. Furthermore, this technology can be combined with traditional polymerase chain reaction (PCR) and other techniques to facilitate on-site detection of Foc4.

Lifting and transportation equipment can greatly contribute to the harvesting of mountain bananas in the agricultural industry. However, some challenges still remain during post-harvest transportation, such as high labor intensity and cost, easy damage to fruits, and safety accidents. In this study, an Unmanned aerial vehicle (UAV) autonomous lifting and transportation equipment was designed to harvest the mountain bananas. The autonomous grasping and unloading of banana shafts were realized during the lifting and transportation. Manual participation was effectively reduced to ensure the safety of operators. The grasping mechanism, lifting gear, guide mechanism, and connecting parts were designed, according to the measured morphological features of the mountain banana. The specific needs were fully met by the postharvest transportation of mountain bananas. A three-dimensional model of the equipment was established using SOLIDWORKS software. The communication system of the upper and lower machines was also built. The key components of lifting and transportation equipment were simulated and theoretically calculated, using the explicit dynamics and magnetostatic analysis of ANSYS Workbench software. The parameters of an electromagnet and steering gear were determined to meet the requirements. The simulation highlighted that the better performance of equipment was achieved for subsequent testing. A laboratory experiment was then carried out to validate the efficacy of the equipment. The success rates of lifting and transportation were 92.59%, 96%, and 88.89%, respectively. The average time of grasping and unloading was 63.8 and 20.8 s, respectively. The effectiveness of equipment was verified to transport the mountain bananas. Furthermore, the field experiment UAV was carried out on the lifting and transportation equipment. The success rate of lifting and transportation was 83.33%, the total time of grasping and unloading was 90.8 s, and the average speed of lifting and transportation was 0.99 m/s, which was more than three times that of manual carrying speed (0.17~ 0.33 m/s). Because the banana bunch was suspended under the equipment without contacting with other objects, there was no damage to the fruit finger, indicating the better quality of the fruits during transportation. Nevertheless, the equipment produced a large lateral swing on the positioning and grasping of the banana shaft, due mainly to the strong wind field under the UAV. Therefore, there were some differences between the field test and the indoor experiment, but the expected performance was achieved anyway. The structure can be further optimized to improve the success rate of lifting and transportation of the equipment for less operation time. In conclusion, the equipment can fully meet the operational requirements for the autonomous lifting and transportation of mountain bananas by UAV. Overall, these findings can provide a strong reference for efficient, low-loss, and safe unmanned lifting and transportation equipment. A reliable and efficient solution can be offered for autonomous lifting and transportation in the banana industry towards sustainable growth and productivity.