A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant’s growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers

Wetting and deposition characteristics of air-assisted spray droplet on large broad-leaved crop canopy

Precision and efficient pesticide spraying is an important part of precision agriculture, banana is a large broad-leaved plant, with pests and diseases, has a high demand for spraying and pest control. The purpose of this study was to clarify the wettability of different pesticides on the banana leaf surface, and the effects of nozzle type and working parameters on the deposition distribution performance under air-assisted spray conditions. The wettability test results of different pesticides on banana leaf surfaces showed that the wettability of the adaxial side was always stronger than that of the abaxial side, the smaller the surface tension of the droplets, the better the wettability on the surface. The spray experiment was carried out on the previously developed air-assisted sprayer with the latest developed intelligent variable spray control system. Three types of nozzles were used to spray with different combinations of working parameters. The deposition distribution performance on the banana leaf surface was obtained by image processing using a self-compiled program. The experimental results show that the nozzle type, wind speed, and spray pressure have significant effects on the deposition distribution performance. Through the study of the interaction and coupling effect of nozzle type and working parameters on the spray droplet deposition distribution on both sides of banana leaves, the results show that under the conditions of hollow cone nozzle, 0.5Mpa spray pressure and 3-5 m/s wind speed, the spray coverage and droplet density are in the optimal state. This is mainly due to the low spray pressure and/or wind speed is not enough to make the banana leaves vibrate and improve the performance of pesticide deposition. excessive spray pressure and/or wind speed will cause large deformation of banana leaves and make them airfoil stable, which reduces the surface deposition performance. It is of great significance for promoting sustainable and intelligent phytoprotection

Mechanized Technology Research and Equipment Application of Banana Post-Harvesting: A Review

In all operations of banana post-harvesting, picking and transportation are the operations with the highest labor intensity and the highest production costs. The operation of banana de-handing is still in the traditional stage by manual cutting, which seriously impedes the development of the banana industry towards mechanization, automation and intelligence. This review summarizes and analyzes the mechanized technology research status and equipment application of the banana picking, transportation and de-handing operations in banana orchards around the world and proposes basic ideas and constructive suggestions for quickening the mechanized development of banana post-harvesting

De-Handing Technologies for Banana Postharvest Operations—Updates and Challenges

Many aspects of the agricultural industry such a field crop planting and harvesting and chemical application in fruit crops have been employing mechanization and automation solutions for decades. However, the de-handing operation in banana postharvest operations is usually performed manually. Mechanical or automated de-handing is a potential long-term solution to address labor shortages and the associated high costs. Bananas are mainly grown in developing countries located in tropical and subtropical regions, where the development of agricultural mechanization and automation solutions started only recently and is progressing relatively slowly. In addition, large-scale banana orchards are mainly distributed in hilly and mountainous areas, though there are also some small-scale banana plantations in plain areas. The complex environment of banana orchards and the aging farming population are other important factors that make it difficult to realize mechanized operation of banana de-handing. In recent years, researchers have proposed advanced techniques that may facilitate the development of mechanical de-handing systems. However, the successful adoption of mechanical de-handing technology still faces many challenges. This paper systematically reviews the existing research on de-handing technologies and component mechanisms. A comprehensive evaluation is carried out from the perspectives of feasibility of the mechanism design, stability of the model simulation and reliability of the prototype systems developed. The future challenges and opportunities for designing and practically adopting mechanical de-handing equipment are also summarized and discussed

Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm

Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems

Banana Leaf Surface’s Janus Wettability Transition from the Wenzel State to Cassie–Baxter State and the Underlying Mechanism

Janus wettability plays an important role in certain special occasions. In this study, field emission scanning electron microscopy (FESEM) was used to observe the surface microstructure of banana leaves, the static wettability of the banana leaf surface was tested, and the dynamic response of water droplets falling at different heights and hitting on the adaxial and abaxial sides was studied. The study found that the nanopillars on the adaxial and abaxial sides of the banana leaf were different in shape. The nanopillars on the adaxial side were cone-shaped with large gaps, showing hydrophilicity (Wenzel state), and the heads of the nanopillars on the abaxial side were smooth and spherical with small gaps, showing weak hydrophobicity (Cassie–Baxter state). Banana leaves show Janus wettability, and the banana leaf surface has high adhesion properties. During the dynamic impact test, the adaxial and abaxial sides of the banana leaves showed different dynamic responses, and the wettability of the adaxial side of the banana leaves was always stronger than the abaxial side. Based on the structural parameters of nanopillars on the surface of the banana leaf and the classical wetting theory model, an ideal geometric model around a single nanopillar on both sides of the banana leaf was established. The results show that the established model has high accuracy and can reflect the experimental results effectively. When the apparent contact angle was 76.17°, and the intrinsic contact angle was 81.17° on the adaxial side of the banana leaf, steady hydrophilicity was shown. The abaxial side was similar. The underlying mechanism of Janus wettability on the banana leaf surface was elucidated. This study provides an important reference for the preparation of Janus wettability bionic surfaces and the efficient and high-quality management of banana orchards

Optimization-Design and Atomization-Performance Study of Aerial Dual-Atomization Centrifugal Atomizer

The aerial atomizer is the most essential component of the plant protection UAV (unmanned aerial vehicle). However, the structural optimization of existing aerial atomizers lacks comprehensive consideration of spray parameters and structural parameters, and there is a shortage of available atomizer spray models, resulting in the unstable effect of UAV application. In our previous work, an aerial dual-atomization centrifugal atomizer was developed. In order to obtain an aerial atomizer with good atomization effect and its atomization model, structural optimization at different rotation speeds and flow rates of the atomizer, and its atomization performance, are studied in this paper. Firstly, with the droplet volume median diameter (VMD) and spectral width (SRW) as the evaluation index, through the single-factor, Plackett–Burman and Box–Behnken tests, the influence of rotation speed, flow rate, tooth number and tooth shape were studied. The regression models of the droplet VMD and SRW were established using multiple quadratic regression fitting of the test data. Secondly, in order to achieve the lowest droplet VMD and SRW, the response surface method and post-hoc multiple comparison method were used to obtain the optimized structure of the atomizer’s rotation ring at different rotation speeds (600–7000 r/min) and flow rates (500–1000 mL/min). Lastly, with the effective swath width (ESW) of the optimized atomizer as the evaluation index, through the Box–Behnken test, the influence of rotation speed, flow rate and spray height were studied. The multiple quadratic regression model of ESW was established with the test data. The test results indicated that rotation speed, flow rate and tooth number had a significant effect on droplet VMD and SRW; tooth shape had no significant effect on the droplet VMD and SRW, however, the square tooth shape had the best atomization effect; and rotation speed, flow rate and spray height had a significant effect on ESW. The optimized structural parameters were tooth shape: square, and tooth number: 20. The determination coefficient R2 of the regression model of VMD, SRW and ESW were 0.9976, 0.9770 and 0.9974, respectively, which indicates that the model was accurate, and can evaluate and predict the spray effect. This paper provides an optimized dual-atomization centrifugal atomizer, and its regression models of VMD, SRW and ESW for UAV applications can provide a reference for efficient UAV spraying

Three-Dimensional Structure Measurement for Potted Plant Based on Millimeter-Wave Radar

Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millimeter-wave radar and evaluate the system on plants in static, rotating, and rotating-while-spraying states. The position and rotation speed of the rotating platform are used to compute the rotation–translation matrix between point clouds, enabling the multi-view point clouds to be overlaid on the world coordinate system. Point cloud extraction is performed by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), while an Alpha-shape algorithm is used for three-dimensional reconstruction of the canopy. Our measurement results for the 3D reconstruction of plants at different growth stages showed that the reconstruction model has higher accuracy under the rotation condition than that under the static condition, with average relative errors of 41.61% and 10.21%, respectively. The significant correlation between the sampling data with and without spray reached 0.03, indicating that the effect of the droplets on radar detection during the spray process can be neglected. This study provides guidance for plant canopy detection using millimeter-wave radar for advanced agricultural informatization and automation

Study of Microstructure and Properties of 316L with Selective Laser Melting Based on Multivariate Interaction Influence

The selective laser melting technique is widely used in aerospace and biomedical industries, and the performance of formed 316L parts is significantly subject to the forming angle. As the selective laser melting 316L parts are constrained by multiple performance indexes, the study involves multivariate interaction influenced on the forming parameters such as the angle with the xz plane, the angle with the xy plane, laser power, scan speed, powder thickness, and hatching space on the indexes like tensile strength, density, and surface roughness with linear regression equations based on multiobjective optimization to obtain the best process parameters. The study results of microstructure performance of the formed 316L parts show that the angle with the xz plane has significant effect on the experiment indexes, while the layer thickness has the greatest effect on the indexes. After stretching, the molten pools are obviously elongated and the microstructure of the formed 316L parts is composed of equiaxed crystals and columnar crystals with a grain width of 0.28–0.4 nm. The secondary growth of the dendrites is not obvious, and the crystallinity of the selective laser melting 316L parts is not as good as the standard parts, with the microstructure showing directional solidification due to grain refinement and microscopic distortion of crystals. As the fracture has dimples, it is a ductile fracture and typical plastic fracture. The hardness near the fracture is higher than that of the substrate, whilst the indexes regarding the selective laser melting parts are higher than the ASTM-A182 and ASTM-F3184-16 standards. Since the theoretical model built in this study has less error, the findings have practical engineering application value

Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion

Deep learning is the subject of increasing research for fruit tree detection. Previously developed deep-learning-based models are either too large to perform real-time tasks or too small to extract good enough features. Moreover, there has been scarce research on the detection of pomelo trees. This paper proposes a pomelo tree-detection method that introduces the attention mechanism and a Ghost module into the lightweight model network, as well as a feature-fusion module to improve the feature-extraction ability and reduce computation. The proposed method was experimentally validated and showed better detection performance and fewer parameters than some state-of-the-art target-detection algorithms. The results indicate that our method is more suitable for pomelo tree detection