Harvesting Process Research Articles

Cultivation and Processing of Cimanggu Cassava Harvest Waste at SMK Negeri I Borong, East Manggarai Regency

Cassava is one of the agricultural crops that has been widely cultivated in the East Nusa Tenggara region. The cimanggu variety is a cassava variety that is quite tolerant to drought and can produce 80 tons/ha. Additionally, the cimanggu variety is one of the most suitable cassavas for mocaf production. The East Manggarai Regency area is one of the fertile regions in NTT suitable for cassava cultivation, especially Cimanggu cassava. This research aims to understand the cultivation techniques of Cimanggu cassava in the Borong area and the role of SMK Negeri I Borong as a region for planting Cimanggu cassava. In addition, to understand the waste processing techniques generated during the harvesting process of Cimanggu cassava. The data collection method used a qualitative approach with data collection through field observation techniques. The research results show that the cultivation of cimanggu cassava plants carried out by students of SMKN 1 Borong includes land preparation (soil loosening), preparation of cimanggu cassava seeds, planting methods, planting distance, fertilization, weeding, and harvesting. The waste from the harvest of cimanggu cassava, in the form of cassava leaves and cassava peels, is processed into fermented livestock feed for animals.

Thickness monitoring of threshing mixture on the oscillating plate of corn grain harvester

Cleaning is a critical operation in the corn grain harvesting process, and the operation is seriously affected by the uneven distribution of the threshing mixture on the oscillating plate. To monitor the thickness of the threshing mixture on the oscillating plate of the corn grain harvester, in this study, we adopted the lever principle to design a corn threshing mixture thickness monitoring device based on the piezoresistive effect and theoretically analyzed the lever impact mechanics. The factors affecting the force on the sensor of the monitoring device are the vibration frequency of the oscillating plate, the thickness of the threshing mixture, and grain moisture content, which were obtained through theoretical analysis. The operational performance of the corn threshing mixture thickness monitoring device was assessed through a set of single-factor experiments, revealing the influence laws of three factors on the impact voltage: vibration frequency, thickness of the threshing mixture, and grain moisture content. A back-propagation (BP)11Back-propagation (BP) neural network prediction model was established using an orthogonal experiment with impact voltage, vibration frequency, and grain moisture content as input layers and the thickness of the corn threshing mixture as the output layer. An STM32 microcontroller was used as the primary control unit to build a thickness monitoring data acquisition and processing system that could process and operate the impact voltage collected by the pressure sensor and the vibration frequency signal collected by a Hall sensor. The data were wirelessly transmitted to a PC to predict the thickness of the threshing mixture by using the trained BP neural network. The experimental results of monitoring the variation in corn threshing mixture thickness demonstrated that the device could effectively monitor changes in thickness. The monitoring accuracy of the monitoring device was found to be over 90%, and the system can be used for monitoring the threshing mixture thickness on the oscillating plate of the corn grain harvester.

A Comprehensive Methodology for Image Recognition Utilizing Machine Learning and Computer Vision: Automation of the Harvesting Process

This study aims to investigate the machine learning techniques implemented in image recognition technology for the identification and classification of oil palm fruit ripeness. The accurate determination of fruit ripeness is crucial for optimizing harvest time and improving oil yield. The palm oil industry is one of the major plantations in Malaysia. The harvesting process of oil palm fruit was conducted with traditional methods by relying on manual inspection, which can be subjective and inconsistent. Plus, it required several workers. A model of image recognition was developed using machine learning algorithms and computer vision to automate the harvesting process and overcome the shortage of labor issues. Implementing this technology in the field could lead to more consistent harvests and higher-quality oil production. Several machine learning models were developed, trained, and tested for their ability to classify the ripeness stages. The findings suggest the trending techniques in implementing image recognition which can provide a reliable and efficient tool for assessing oil palm fruit ripeness.

FRESH: Fusion-Based 3D Apple Recognition via Estimating Stem Direction Heading

In 3D apple detection, the challenge of direction for apple stem harvesting for agricultural robotics has not yet been resolved. Addressing the issue of determining the stem direction of apples is essential for the harvesting processes employed by automated robots. This research proposes a 3D apple detection framework to identify stem direction. First, we constructed a dataset for 3D apple detection that considers the 3-axis rotation of apples based on stem direction. Secondly, we designed a 3D detection algorithm that not only recognizes the dimensions and location of apples, as existing methods do, but also predicts their 3-axis rotation. Furthermore, we effectively fused 3D point clouds with 2D images to leverage the geometric data from point clouds and the semantic information from images, enhancing the apple detection performance. Experimental results indicated that our method achieved AP@0.25 89.56% for 3D detection by considering apple rotation, surpassing the existing methods. Moreover, we experimentally validated that the proposed loss function most effectively estimated the rotation among the various approaches we explored. This study shows the effectiveness of 3D apple detection with consideration of rotation, emphasizing its potential for practical application in autonomous robotic systems.

Assessment of timber and fuel wood harvesting practices and distribution pattern in CFUGs of Khotang District, Nepal

Proper harvesting of forest products is necessary for sustainable forest management, playing a pivotal role in minimizing damage and waste while ensuring the sustainability of forest resources. Sustainable community forest management greatly depends on the harvesting and fair distribution of various forest products (FP). The study's goals were to ascertain the methods, instruments, and strategies utilized in the harvesting process, forest products distribution pattern, and the common issues surrounding the distribution and harvesting of FP in Imo and Ashoje Community Forest (CF) located in Khotang District, Nepal. Operational Plan (OP), focus groups, key informant interviews, household surveys, and minute books were used for data collection. 25% of all households were chosen at random from each of the two CF. The study revealed that traditional methods and tools such as sickles, bill hooks, axes, and saws, were commonly used in the past; however, there has been a shift towards modern tools in recent years. Deviations were observed in forest products harvesting and distribution from that of OP in practice. Fuel wood were equally distributed, timbers were allocated based on priority and circular basis ensuring user satisfaction. The slope was the major constraint for harvesting, as most of the marked trees were not cut because of difficulty in extraction. To improve sustainable forest management, it is recommended that advanced tools and training be provided to users, with regular monitoring by forest officials to ensure adherence to best practices. These measures will help enhance user responsibility and ensure sustainable harvesting operations.

Biomechanics of Negative-Pressure-Assisted Liposuction and Their Influence on Fat Regeneration.

Autologous fat grafting has been widely adopted in cosmetic and reconstructive procedures recently. With the emerging of negative-pressure-assisted liposuction system, the harvesting process of fat grafting is more standardized, controllable, and efficient. Each component in the system could influence the biomechanical environment of lipoaspirate. Several reviews have studied the impact of negative pressure on fat regeneration. As the initial part of the harvesting system, cannulas possess their unique mechanical parameters and their influence on lipoaspirate biomechanical characters, biological behaviors, and regeneration patterns remains unclear. Basic invivo and in vitro studies have been performed to determine the possible mechanisms. Instant invivo studies focus on adipocytes, stromal vascular fraction cells, fat particles, and growth factors, while invivo grafting experiments analyze the graft retention rate and histology. Understanding the different regeneration patterns of lipoaspirate and the mechanisms behind may facilitate the choice of harvesting cannulas in clinical practice.

Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models

Accurate timber quantification is essential in forestry and the timber industry, impacting harvest planning, processing, pricing, and overall supply chain management. Traditional methods for estimating the volume of stacked timber, often reliant on manual measurements, are time-consuming and prone to error. This research aims to develop an accurate procedure for estimating the volume of stacked eucalypt timber in yards. The proposed procedure combines automatic log detection and diameter distribution models. Automatic log detection was achieved using advanced computer vision techniques, specifically the You Only Look Once version 9 (YOLOv9) model, which automates the identification and counting of individual logs within a stack. We used stem diameter distribution models to estimate total stack volume based on log counts and probability densities. This approach ensures high accuracy and efficiency, significantly reducing the time and effort required for volume estimation. The dataset used for this study includes diameter measurements from a pre-harvest inventory of eucalypt trees aged 6 and 7 years, alongside videos of stacked timber. The YOLOv9 model was trained to detect logs from these videos, achieving high precision in object detection and segmentation tasks. Performance metrics such as Box Precision, Box Recall, and mean Average Precision (mAP) were used to evaluate the model's effectiveness. The results indicate that the model generalizes well to new data, with high accuracy in both validation and test sets. Among the distribution models evaluated, the generalized extreme value (GEV) distribution provided the best fit for the stem diameter data, allowing for accurate volume predictions. This procedure, which integrates automatic log detection with diameter distribution models, offers a scalable solution applicable to large and complex timber stacks. Finally, a repository was established to allow users to test the proposed method. Future works will focus on refining the model's accuracy and expanding its applicability across different species, forest production and log conditions.

Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology

In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy.

Nanomaterials for Energy Conversion

Nanomaterials have emerged as a cornerstone in advancing energy conversion technologies, offering unparalleled potential for improving efficiency and sustainability. Their unique properties—such as high surface area, tunable electronic structure, and quantum effects—enable superior performance in applications like solar cells, fuel cells, thermoelectric devices, and batteries. By manipulating nanoscale architectures, researchers can enhance energy harvesting, storage, and conversion processes. This study explores the role of nanomaterials in energy conversion, focusing on their design, synthesis, and application in cutting-edge technologies. The integration of nanomaterials with advanced energy systems has shown significant potential to address challenges such as limited energy resources and environmental concerns. Special emphasis is placed on nanostructured semiconductors, quantum dots, and graphene-based materials for their contributions to photovoltaic and catalytic processes. Despite their advantages, challenges remain, including scalability, stability, and cost-efficiency. This research aims to provide a comprehensive overview of recent advancements, evaluate existing challenges, and highlight future directions for nanomaterials in energy conversion, ultimately contributing to a sustainable energy future.

Applications of bio-composites in electronics

Abstract Functional biomaterials are being used in many promising industries to improve human quality of life and advance environmental objectives. Consideration has been given to many applications in the domains of medical, electronics, food, and pharmaceuticals. The use of bio-inspired materials enables the creation of more sustainable alternatives that strive to advance environmental preservation while simultaneously ensuring customer satisfaction. It was discovered that biopolymers are used in a number of different industries for the production of a wide range of functional bio-products. These bio-products include organic thin film transistors, organic phototransistors, emitting diodes, photodiodes, photovoltaic solar cells, hybrid dental resins, sustainable medicines, and consumer food packaging. A growth of this magnitude makes it possible to conduct substantial research in order to more inspection of the limitless requests and uses of bio-based composites. In order to fulfill the needs of certain applications, it is necessary to adjust and reassess attributes and parameters– such as hardness, durability, crack toughness, binding, solubility, polarization, plasticity, hydrogen bonding, thermal characteristics, and dielectric behavior. By virtue of their electronic and electrical properties, bio-composites and biopolymers have been put to use in a variety of applications; some includes organic thin-film transistors, electrical applications, electromagnetic insulation, energy harvesting, and thermoelectric processes. Substantial proportions of electronic waste, also referred to as E-waste, are regularly released into the environment due to the continuous growth in the production of electronic devices. Consequently, this leads to substantial environmental and ecological problems caused by the release of non-degradable polymers, hazardous compounds, and toxic heavy metals into the environment. The advancement of biodegradable polymers has significant potential for effectively reducing the environmental burden, since they may be decomposed or absorbed into the surrounding environment without generating any toxic effects. Hence, the purpose of this study is to illustrate the creation of biocompatible composites and their prospective uses in electrical applications.

Analytical Design and Test of Licorice Harvester Based on DEM–MBD Coupling

This study addresses the issues of high operating resistance, incomplete separation in ascending transport chains, and significant wear and tear in existing licorice harvesters. A new licorice harvester has been designed that incorporates a lift chain conveyor separation device, enabling excavation, separation, collection, and centralized stacking to be completed in a single operation. The paper describes the harvester’s overall structure and provides detailed analyses and designs of its key components, including the digging shovel, roller screen, conveying and separating screens, and soil-crushing roller. Multi-body dynamics (MBD) and discrete element models (DEM) for licorice and soil were developed, and the entire harvesting process was simulated using the coupled DEM–MBD method to analyze the trajectory and speed of the licorice. Field tests confirmed that the conveyor separation screen operates smoothly, effectively separates licorice rhizomes from soil, and minimizes damage to the licorice. Field test results show a net digging rate of 96.2%, a damage rate of 4.3%, and an average digging depth of 580 mm. The operational indexes meet the standards for harvesting root and stem Chinese herbal medicines. The machine operates stably and exhibits exceptional conveying and separating effects, demonstrating its suitability for mechanized harvesting of root and stem herbs.

Super Moisture-Sorbent Sponge for Sustainable Atmospheric Water Harvesting and Power Generation.

Sorption-based atmospheric water harvesting (SAWH) shows great promise to mitigate the worldwide water scarcity, especially in the arid regions. Salt-based composite materials are the extensively used sorbents for SAWH, however, they suffer from complex preparation to avoid salt leakage. Furthermore, the significant amount of heat produced during water harvesting process is often neglected and wasted. Herein, an integrated strategy is developed to synthesis salt-based stable super moisture-sorbent sponge by using the chelation of LiCl and dopamine (DA), and the simultaneous polymerization of DA on melamine sponge (PMS). The as-prepared LiCl/PMS/CNTs showed high water uptake, reaching 1.26 and 1.81g g-1 at 15% and 30% RH, respectively, and no salt leakage is observed during the water absorption process. Remarkable daily water production of 3.47kg kg-1 day-1 in an arid environment (30% RH) is achieved. Moreover, a dual-function system is successfully constructed by combining the LiCl/PMS/CNTs with a thermoelectric module to fully utilize the heat generated from the SAWH process, which can realize the simultaneous production of fresh water and electricity. The maximum output power density is up to 35.4 and 454.4mW m-2 during the water absorption and desorption process, respectively.

Progress in Agronomic Crops Lodging Resistance and Prevention: A Review

ABSTRACTCrop lodging is recognised as one of the yield‐limiting factors in agricultural production. Therefore, better understanding to improve lodging resistance and to prevent lodging‐induced losses in agronomic crops is necessary. Besides yield losses, lodging severely affects the crop harvesting process and increases the production cost. However, achieving the objective of higher crop yields and yield quality without increasing lodging risk is quite challenging. To this end, it is essential to interpret the underlying mechanism of plant stem buckling and failure of root anchorage and optimise the fundamental trade‐off between lodging resistance and yield performance in agronomic crops. In the present review, we made an effort to discuss recent and innovative research insights that guarantee greater lodging resistance along with advanced lodging prevention strategies while sustaining higher crop yield and yield quality.

Neuromorphic Computing for Smart Agriculture

Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this paper is to describe the current principles and development of the neuromorphic computing technology, explore the potential examples of neuromorphic computing applications in smart agriculture, and consider the future development route of the neuromorphic computing in smart agriculture. Neuromorphic computing includes artificial synapses, artificial neurons, and artificial neural networks (ANNs). A neuromorphic computing system is expected to improve the agricultural production efficiency and ensure the food quality and safety for human nutrition and health in smart agriculture in the future.

Effects of water deficit stress and salicylic acid preharvest treatments on postharvest biochemical parameters of potato

Harvest conditions, harvest state and the pre-treatments to which the potato plant is subjected to affect post-harvest quality. The present study was conducted to verify the effect of the SA foliar application challenged with water deficit stress (WDS, 45 kPa) and control (10 kPa). After the harvest process, the tubers of both treatments were treated with a SA solution (8mM). Tubers were stored in a dark room at temperature (20 ± 3.5 °C) with relative humidity (65 ± 5 RH) for a period of 60 days, evaluated with different biochemical parameters. The results showed that foliar application of SA presented positive effects in the mitigation of oxidative damage through the dismutation of reactive oxygen species (ROS), suggesting the protection of the cellular membrane structure. Furthermore, there was less accumulation of malondialdehyde and synthesis of bioactive compounds, which certainly increased the antioxidative capacity of plants. WDS tuber was the treatment that presented the highest antioxidant activity with 1.82µM FeSO4 mg-1. In general, the results indicated that the foliar application of SA and immersion with the same acid in post-harvest presented a positive effect in the biochemical parameters.

Osteophyte of the first costochondral joint is a reliable landmark to facilitate standardized robotic left internal mammary artery harvesting

ObjectiveRobotic left internal mammary artery (LIMA) harvesting can present various challenges for novice surgeons. This study aimed to investigate whether the osteophyte of the first costochondral joint (OFCCJ) could serve as an anatomical landmark for robotic LIMA harvesting and to explore the optimal standardized process. MethodsThe concept of “LIMA isthmus” was described on the basis of relatively fixed anatomical relationship between LIMA and OFCCJ, and the height of OFCCJ and its distance to LIMA were defined. The presence and features of OFCCJ were analyzed in 514 hospitalized patients with or without coronary artery diseases (CAD or non-CAD). The value of “LIMA isthmus”-based robotic harvesting strategy was further assessed for beginners during the learning curve phase. ResultsThe OFCCJs were found to be very common in the adult population. The prevalence of OFCCJ in patients with CAD was significantly greater than in patients without CAD (73.16% vs 60.33%, P < .01). However, there were no significant difference between the CAD and the non-CAD group in the height of the OFCCJ or the distance between the OFCCJ and the LIMA (15.54 ± 4.99 mm vs 16.55 ± 4.70 mm, P = .06; 12.66 ± 4.19 mm vs 12.45 ± 6.87 mm, P = .72). During the learning curve phase, the time to identify LIMA and the time for LIMA harvesting using “LIMA isthmus”-based strategy were significantly shorter than those with conventional approach (89.00 ± 31.07 seconds vs 1087 ± 436.1 seconds, P = .035; 86.80 ± 4.74 minutes vs 110.5 ± 11.72 minutes, P = .013). The inadvertent bleeding events with “LIMA isthmus”-based graft harvesting were much fewer (1.00 ± 0.19 vs 3.44 ± 0.34, P < .001), although there was no difference between 2 beginners in postoperative chest drainage, LIMA injury events, or conversion to a median sternotomy incision. ConclusionsOFCCJ could serve as a reliable anatomical landmark to guide robotic LIMA harvesting. “LIMA isthmus”-based standardized robotic LIMA harvesting could significantly reduce the time to determine LIMA and the time for graft harvesting process, and minimize the risk of inadvertent bleeding events. This could be particularly beneficial for novice surgeons during the initial learning curve phase of this procedure.

ASSESSMENT OF LOSSES IN THE SUGARCANE HARVESTING PROCESS IN THE IVINHEMA VALLEY REGION

Brazil has approximately 18% of its entire arable land destined for sugarcane production. This raw material is of great economic importance to the country, with production of by-products with high added value, such as sugar, alcohol and ethanol. Some states with larger territorial extension are responsible for large-scale production, mainly in the Central-West, Southeast and North regions of the country. The most prominent state, with 53% of all sugarcane cultivation, is São Paulo, followed by Minas Gerais, Goiás and Mato Grosso do Sul. Given this, with large-scale production, other challenges begin to emerge, such as the need for cultivation within technological aspects that prevent the occurrence of losses in the production system, from the establishment of sugarcane fields and especially their harvesting and transportation, until the arrival of the raw material within the industrial complex for transformation into by-products. The objective of this study was to analyze and quantify the percentage of losses between the harvesting process and receipt in the industry of freshly harvested sugarcane raw material in the Ivinhema Valley region - Mato Grosso do Sul. The methodology used was a case study, which comprised three months of analysis. With the data analyzed, it was possible to verify that the highest percentage of losses occurs for the portion of producers who carry out their own cultivation, sending their raw material to the industry, generating around 44.02% in losses between the total harvested and received. In addition, harvests further away from the industrial complex presented greater losses of around 9.85% in mineral and vegetable impurities, evidencing that transportation may have influenced the time of receipt.

Influence of donor and harvesting protocol on 30 day mortality after heart transplantation

Abstract Purpose Heart transplantation continues to be the gold standard for the treatment of advanced HF, as it promotes increased survival, exercise capacity and quality of life. The choice of the potential donor, as well as the safety of the harvesting process, graft preservation and transportation are fundamental to the success of the procedure. With the territorial extension of Brazil and a large number of donations in other states and regions, evaluating donors remotely, as well as structuring an adequate fundraising plan are fundamental. Therefore, the study of these variables and their influence on the outcome at 30 days was the objective of this study. Methods A retrospective cohort was conducted involving patients aged 18 or over, who underwent heart transplantation from July 2012 to July 2022 at our center. Patients who underwent re-transplantation and those who not used the bicaval technique were excluded. Statistical analysis was performed using STATA Software version 18. The Kaplan-Meier method was used to obtain the overall survival curve and, to identify risk factors for death, Cox proportional hazards. The Log test -rank was used for comparisons between groups. Results 255 cases of heart transplantation were studied, with a 30-day survival rate of 86.6%. The average age of donors was 29 years old and the main cause of death was traumatic brain injury.Regarding the factors that increase the risk of mortality in 30 days, the longest CPB time (HR 1.01; 95% CI 1.002-1.014; p = 0.011) and the donor's age (HR 1.08; 95% CI 1.023-1.139; p = 0.005) were statistically significant. In the univariate analysis, the donor's nutritional status (overweight or obese) was also significant (p = 0.03).The donor's length of hospital stay, use of vasoactive drugs or antimicrobials, serum sodium level or history of previous PCR did not influence the recipient's 30-day survival. Distance organ retrival surgery did not influence the outcome as long as the ischemia time of less than 240 minutes was respected. Conclusion The distance harvesting protocol used in our institution proved to be safe and effective. Adequate evaluation of the donor through echocardiography as well as management of vasoactive drugs and electrolytes may have influenced the non-relevance of classic factors that affect the outcomes of patients after heart transplantation.

Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging

Lodging, a prevalent issue during rice growth, detrimentally impacts both yield and quality. It also complicates the harvesting process, reducing the efficiency of mechanized collection. Existing monitoring methods, predominantly based on manual observation and satellite remote sensing, fall short in addressing the requirements of contemporary, efficient, and real-time agriculture. This research integrates image analysis techniques with advanced optimization algorithms to develop a semantic segmentation model specifically designed for detecting rice lodging in remote sensing images. The model, named MI-UConvNeXt, employs a ConvNeXt-based feature extraction network utilizing UNet architecture (UConvNeXt) and incorporates an improved multi-objective salp swarm algorithm with Latin hypercube sampling and an elite opposition-based learning strategy (ISSA-LE) to dynamically adjusting the number of UConvNeXt channels. MI-UConvNeXt achieves a balance between accuracy and complexity. Compared to seven other semantic segmentation models from the literature, MI-UConvNeXt exhibits enhanced performance, with a Pixel Accuracy (PA) of 95.59%, mean Pixel Accuracy (mPA) of 95.62%, and mean Intersection over Union (mIoU) of 91.91% on the validation set. This demonstrates the model’s superior accuracy, lower computational resource demands, and enhanced efficiency. By integrating deep learning with intelligent optimization algorithms, this study offers a novel and effective approach for monitoring crop lodging in agricultural production, providing robust technical support for the accurate extraction of crop phenotypic information.

Design and Simulation Study of Structural Parameters of Bionic Cutters for Tea Harvest Imitating Aeolesthes induta Newman

The cutter of the hand-held tea picker is the key cutting component in the efficient tea harvesting process. In order to solve the problems of large cutting resistance and uneven incision during tea picking, this study fully applied the bionics principle to combine the excellent cutting performance of Aeolesthes induta Newman’s mandibles with the tea cutter, which extracted and fitted the tooth profile structure curve of the upper edge of the Aeolesthes induta Newman’s mandibles. The trapezoidal teeth on the reciprocating cutter of ordinary hand-held tea-picking harvesters were optimized by the fitted curve, and a new tea cutter with the shape of Aeolesthes induta Newman teeth was obtained, which included four kinds of bionic tea-harvesting cutters. The multi-body system software ADAMS 2020 and finite element analysis software ANSYS 2024R1 were used to compare the kinematics, statics and explicit dynamics of cutting properties of the four bionic cutters and common cutters with ordinary trapezoidal teeth and saw teeth. The simulation results showed that the maximum equivalent elastic strain and the maximum cutting force during the cutting operation were reduced by 36.7% and 42.89%, respectively, for the cutting teeth of the bionic tea-harvesting cutter #4 compared with that of the cutter with ordinary trapezoidal teeth. The bionic tea-harvesting cutter designed in this study has better cutting performance than the cutter with traditional cutting teeth, which can effectively reduce the cutting force and improve the flatness and cutting quality of the cutting surface.