Improved Harvesting Efficiency Research Articles

Revolutionizing Palm Dates Harvesting with Multirotor Flying Vehicles

This study addresses the challenges of traditional date palm harvesting, which is often labor-intensive and hazardous, by introducing an innovative solution utilizing multirotor flying vehicles (MRFVs). Unlike conventional methods such as hydraulic lifts and ground-based robotic manipulators, the proposed system integrates a quadrotor equipped with a winch and a suspended robotic arm with a precision saw. Controlled remotely via a mobile application, the quadrotor navigates to targeted branches on the date palm tree, where the robotic arm, guided by live video feedback from integrated cameras, accurately severs the branches. Extensive testing in a controlled environment demonstrates the system’s potential to significantly improve harvesting efficiency, safety, and cost-effectiveness. This approach offers a promising alternative to traditional harvesting methods, providing a scalable solution for date palm cultivation, particularly in regions with large-scale plantations. This work marks a significant advancement in the field of agricultural automation, offering a safer, more efficient method for harvesting date palms and contributing to the growing body of knowledge in automated farming technologies.

Scheduling of Collaborative Vegetable Harvesters and Harvest-Aid Vehicles on Farms

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation.

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

Cotton (Gossypium hirsutum) Response to Plant Protection Products using Different Spray Nozzles

Application of dicamba to dicamba-tolerant cotton (Gossypium hirsutism L.) cultivars (Xtend Cotton, Bayer Crop Science, Research Triangle Park, NC) requires use of nozzles that deliver large spray droplets to avoid off-site movement through particle drift onto susceptible crops and other plants including endangered species. Twenty trials over a three-year period were conducted to determine if nozzles required to deliver dicamba plus glyphosate would be equally effective in delivering other pesticides including the herbicide glufosinate, the plant growth regulator mepiquat chloride, the insecticide bifenthrin plus dichrotophos and co-application of ethephon plus thidiazuron plus tribufos for removal of cotton foliage to improve harvest efficiency. Agrichemicals were applied using: a) Air Induction (AI) nozzles for agrichemicals except dicamba plus glyphosate b) Turbo Teejet Induction (TTI) nozzles for all agrichemicals c) TTI nozzles for glufosinate and dicamba plus glyphosate and AI nozzles for other agrichemicals. Applications were made in 145 L/ha at 152 kPa at a ground speed of 5 km/h. Cotton height at the end of the season, node above first fruiting branch, nodes above cracked boll, node of uppermost boll, total nodes, lint yield, and cotton fiber quality were similar regardless of nozzles used to deliver agrichemcials. Control of a complex of broadleaf signal grass (Urochloa platyphylla L.), goosegrass [Eleusine indica (L.) Gaert.], and large crabgrass (Digitaria sanguinalis L.) and Palmer amaranth (Amaranthus palmeri Watts.) was slightly lower when glufosinate was applied with TTI nozzles rather than AI nozzles. However, control of these weeds was similar regardless of nozzle selection when dicamba plus glyphosate was applied after glufosinate. These results suggest that growers can use nozzles that limit off-site movement through particle drift of agrichemicals throughout the cropping cycle with no major adverse effect on weed control and cotton growth and yield.

Low-Wind-Speed Galloping Wind Energy Harvester Based on a W-Shaped Bluff Body

Galloping-based piezoelectric energy harvesting systems are being used to supply renewable electricity for low-power wireless sensor network nodes. In this paper, a W-shaped bluff body is proposed as the core component of a piezoelectric wind energy harvester. Experiments and simulations have shown that the W-shaped bluff body can improve harvesting efficiency at low wind speeds. For the W-shaped structure, the finite element simulation results indicate that the structure can help improve the aerodynamic performance to obtain high aerodynamic force. The experimental results demonstrate that compared with the traditional bluff bodies, the piezoelectric wind energy harvester with the W-shaped bluff body (WEHW) can generate higher output voltages and has a lower cut-in speed. When the length L is 30 mm and the rear groove angle β is 30°, the W-shaped structure can induce the best harvesting performance. When an external load resistance of 820 KΩ is connected and the wind speed is 5 m/s, the WEHW generates an average output power of 0.28 mW.

Improved VIV energy harvesting with a virtual damper–spring system

In recent years, energy harvesting systems driven by vortex induced vibrations (VIV) emerged as potential alternative to conventional rotor-based installations, in particular for low flow speeds in water. Best energy outputs are obtained when the flow-dependent VIV frequency locks in the natural frequency of the energy harvesting structure. However, placed in tidal currents or rivers with variable flow rates, the VIV frequency changes, leading to frequency mismatch with efficiency losses. In this paper, we present a new mechatronic experimental set-up to study the harvesting efficiency of a VIV driven cylinder under various flow rates. In particular, we explore options to improve harvesting efficiency by adding computer controlled virtual damping and stiffness to the mechanical system. The setup comprises an elastically mounted, single-degree-of-freedom cylinder within a low-speed water tunnel. Virtual damping and stiffness are applied by controlling the force exerted through a mechanically coupled DC motor. The experiments reveal that optimal values for damping and stiffness are consistent with previous results from the literature. The maximum energy conversion efficiency of around 18% is achieved when oscillation and vortex shedding frequencies are synchronized. The explored parameter space highlights in particular that mismatches between oscillation and vortex shedding frequency strongly decrease the achievable efficiency. By contrast, the peak efficiency is less sensitive to an increase of damping due to energy harvesting. All these observations suggest that VIV driven energy harvesting systems need a real-time structural stiffness control to maintain an economically acceptable efficiency under various flow conditions. In the near future, we plan to automate the structural stiffness control under various flow rates by testing Machine Learning algorithms with our mechatronic set-up.

Design and experiment of brush-roller ginkgo leaf picker for the dwarf dense planting mode

At present, ginkgo leave are still picked manually. A brush roller ginkgo leaf picker has been designed to improve harvesting efficiency and reduce losses caused by manual failure to pick leaves in time under large-scale planting areas. The ginkgo leaf picker is mainly composed of crawler chassis, gantry frame, brush roller picking parts, and collecting box. The kinematics of the brush roller is analyzed for the picking omission situation. An experimental platform for picking ginkgo biloba leaves was established. Three parameters, namely roller speed, moving speed, and roller inclination, were selected for optimization. Then the maximum net harvest rate and the minimum damage rate were achieved. The orthogonal test showed that when the roller speed was 130 rev/min, the moving speed was 0.25 m/s, and the roller inclination was 32°, the picking effect was the best, the net harvest rate was 93.32%, the damage rate was 1.42%, and the damage degree of the trunks was slight. The experiment proved that the brush-roller ginkgo leaf picker has a good picking effect, which can provide a reference for the optimization design of ginkgo leaf harvesting machinery under the dwarf dense planting mode.

Design and Testing of a Directional Clamping and Reverse Breaking Device for Corn Straw

Realizing high-quality and increased production of fresh corn and promoting diversified development of the corn industry structure not only can effectively promote the development of agricultural economy, but also can enrich people’s dietary culture. However, existing fresh corn machinery has a high rate of ear damage during the harvesting process, and the overall harvesting efficiency is not ideal. To reduce damage during the harvesting of fresh corn, a device for breaking ears of fresh corn was designed based on the directional clamping of corn straw reverse breaking method. Based on the physico-mechanical characteristics parameters of fresh corn ears, the main structural parameters of the directional clamping and conveying mechanism and the ear-breaking mechanism were determined. The overall inclination angle of the device was 15°, and the effective conveying length of the directional clamping mechanism was 550 mm; the ear-snapping mechanism was a snapping roll composed of a pair of six radial distribution function fingers, with an effective operating radius of 320 mm. By simulating and analyzing the reverse breaking movement of directional clamping corn straw, the key motion parameter ranges of the directional clamping conveying mechanism and breaking mechanism were obtained. The results of the bench test showed that under the optimal conditions of a directional clamping feeding speed of 1.67 m/s, a breaking wheel speed of 80 rpm, and a travel speed of 1.06 m/s, the lowest ear damage rate was 0.57%, and the lowest impurity rate was 1.87%. In addition, it was observed that flexible harvesting can improve harvest efficiency and quality. The study also found that actively applying force to the device can effectively avoid the problem of machine blockage and reduce the damage rate of ears (the following text uses ears instead of fresh corn ears).

Increasing Sugarcane Production Eco-Efficiency: A DEA Analysis with Different Sugarcane Varieties

The development of new sugarcane varieties affects crop production positively. However, only some studies have investigated how the use of different sugarcane varieties reacts to the impact of climate change, and how to improve sugarcane production efficiency considering the use of different sugarcane varieties in the field. This research hypothesizes that it is possible to mitigate climate change’s impact on sugarcane production and improve eco-efficiency if the proper sugarcane varieties are chosen. The main objective is to analyze the influence of different sugarcane varieties on production eco-efficiency. An econometric study unveiled the main elements affecting sugarcane production in this research. Afterward, a data envelopment analysis determined the sugarcane varieties with more efficient production. The results indicated that climate impact on production was irrelevant when controlling for different sugarcane varieties. Furthermore, it was found that through correct variety choosing, it is possible to improve harvest efficiency. The outcome of this research contributes to achieving the United Nations SDGs 1, 2, 7, 9, and 15.

AGHRNet: An attention ghost-HRNet for confirmation of catch‐and‐shake locations in jujube fruits vibration harvesting

The development of an intelligent jujube fruit harvesting device is a critical step in achieving the whole mechanization process. Catch‐and‐shake harvesting, as an efficient and stable vibration harvesting method, has widely been used to save labor and improve harvesting efficiency in large-scale jujube orchards. However, existing catch‐and‐shake harvesters still rely heavily on the operator's naked eyes to determine the shaking position, which is subjective, highly inefficient and highly labor intensive. To address this problem, this study proposes a computer vision system including truck and branches segmentation, skeleton extraction and vibration position selection to automatically identify appropriate shaking positions. A lightweight attention Ghost-HRNet (AGHRNet) based on deep learning is designed to separate the truck and branches from the complex orchard background. In AGHRNet, an attention Ghost block is proposed to lighten the model and improve segmentation accuracy. Moreover, we also construct a hybrid loss function to solve the pixel-level category imbalance of the trunks, branches and background. To verify the effectiveness of the AGHRNet, ten state-of-the-art semantic segmentation methods, including the FCN8s, SegNet, U-Net, PSPNet, DeepLabv3+, BiSeNet, DDRNet, DANet, SegFormer and HRNet, are compared. Comparative experimental results show that compared with the other state-of-the-art methods, the proposed AGHRNet has higher segmentation accuracy (77.79 % mIoU, 89.46 % mPA) and a smaller model size (9.53 MB) on our datasets. Finally, mask medial axis extraction and the minimum inscribed circle operation are applied to obtain the appropriate vibration location. The computer vision system can provide technical references for catch‐and‐shake harvesting and developing jujube fruit-harvesting robots.

Exploring the Potential of Flow-Induced Vibration Energy Harvesting Using a Corrugated Hyperstructure Bluff Body.

Fluid-induced vibration is a common phenomenon in fluid-structure interaction. A flow-induced vibrational energy harvester based on a corrugated hyperstructure bluff body which can improve energy collection efficiency under low wind speeds is proposed in this paper. CFD simulation of the proposed energy harvester was carried out with COMSOL Multiphysics. The flow field around the harvester and the output voltage in different flow velocities is discussed and validated with experiments. Simulation results show that the proposed harvester has an improved harvesting efficiency and higher output voltage. Experimental results show that the output voltage amplitude of the harvester increased by 189% under 2 m/s wind speed.

The MYB transcription factor Seed Shattering 11 controls seed shattering by repressing lignin synthesis in African rice.

African cultivated rice (Oryza glaberrima Steud.) was domesticated from its wild progenitor species (Oryza barthii) about 3000 years ago. Seed shattering is one of the main constraints on grain production in African cultivated rice, which causes severe grain losses during harvest. By contrast, Asian cultivated rice (Oryza sativa) displays greater resistance to seed shattering, allowing higher grain production. A better understanding in regulation of seed shattering would help to improve harvesting efficiency in African cultivated rice. Here, we report the map-based cloning and characterization of OgSH11, a MYB transcription factor controlling seed shattering in O.glaberrima. OgSH11 represses the expression of lignin biosynthesis genes and lignin deposition by binding to the promoter of GH2. We successfully developed a new O.glaberrima material showing significantly reduced seed shattering by knockout of SH11 in O.glaberrima using CRISPR-Cas9 mediated approach. Identification of SH11 not only supplies a new target for seed shattering improvement in African cultivated rice, but also provides new insights into the molecular mechanism of abscission layer development.

Design and Experimental Study of a Bionic Blade for Harvesting the Wild Chrysanthemum Stem

Wild chrysanthemum has a high medicinal value. Its mechanized harvest can improve harvesting efficiency, reduce labor costs and improve planting benefits, which is an important way to promote artificial planting. However, one of the difficulties in mechanized harvesting is the large diameter and hardness of the stem, leading to high cutting resistance and power consumption. In order to reduce cutting resistance and power consumption, a bionic cutting blade is designed in this paper by employing the bionics principle and the contour of the cricket’s upper jaw incisor lobe instead of the sharp triangular teeth of the standard harvester blade. Using the finite element method, the cutting-edge angle, cutting angle, and reciprocating speed were taken as test factors. The maximum shear force and power consumption were taken as evaluation indexes. At the same time, the center combination simulation test was carried out to optimize the cutting body and to determine the optimal cutting speed. When the cutting-edge angle was 21°, the cutting angle was 66°, the reciprocating speed was 1.29 m/s, and the maximum shear force and power consumption were minimal. The results showed that the maximum shear force of the bionic cutter was reduced by 18% and the power consumption by 15.8%. The bench test showed that the maximum shear force and power consumption of the bionic cutter were reduced by 10.5% and 10.8%, respectively, when the entire wild chrysanthemum stem was cut. The results can provide a reference for the mechanical harvesting of wild chrysanthemum stems.

PRE-HARVEST DESICCATION FOR PRODUCING HIGH QUALITY COWPEA SEEDS

Pre-harvest desiccation of indeterminate growth habit plants such as cowpea may improve harvest efficiency, uniform maturation and seed quality. This research aimed to determine the effects of pre-harvest desiccation and harvesting time on the yield and quality of cowpea seeds. Two experiments were carried out in Dourados, State of Mato Grosso do Sul, Brazil, in the 2015 and 2017 growing seasons. The experimental design was a split-plot, with main plots arranged as a randomized complete block design, with four replications. Main plots corresponded to the application or no application of paraquat at physiological maturity and subplots to harvesting times: 0, 3, 6. 9, 12 and 15 days after that stage. Moisture content, weight and yield were assessed for each treatment. Physiological quality was evaluated by germination and vigor tests. Means of desiccation conditions were compared by the Tukey test (p 0.05) and harvest times by regression analysis, arranged as a 2x6 factorial. Seed yield and weight were negatively influenced by pre-harvest desiccation with paraquat and delayed harvest. Isolated and interactive responses for desiccation treatments and harvesting time on seed quality varied depending on the growing season. Seed germination and vigor were decreased by late harvest, while desiccation effects varied depending on harvest time.

Work productivity assessment of small forwarders in forest operations: An international review

There are two types of forwarders available in the practice including conventional forestry forwarders and farm tractors equipped with a trailer/grapple loader that operate as a forwarder. Forwarders loading capacity can vary depending on the machine size. This paper aimed to review the work productivity of small size forwarders (payload< 10 tonnes and engine power< 120 HP) to create an overview of their performance for small scale operations within the small-scale forestry/ plantations management or agroforestry systems. According to the review, small forwarders were mainly used for harvesting small trees over gentle slopes. Main machine types include light tractor-trailer and purpose-built forwarders. The reported productivity by international scholars ranged from 3.4 to 19.2 m3/PMH0. Main variables impacting the productivity of small forwarders included forwarding distance, load volume, piece volume, number of logs per turn, season, operator skills and harvesting intensity. This report has summarised recommendations from literatures on proper application of small forwarders to improve harvesting efficiency which can be of use to the forest industrial and academic users for practical and research purposes.

Design of feed rate monitoring system and estimation method for yield distribution information on combine harvester

Real-time monitoring of harvester feed rate is of great significance for guiding harvesting work and improving harvesting efficiency. In this study, a feed rate monitoring system is designed and developed. The system consists of a torque sensor for measuring the header power shaft torque, an angel sensor for measuring header height, a humidity sensor for measuring grains moisture content, a Global Navigation Satellite System receiver, and a vehicle industrial computer. Data, such as the moisture content of grains and the working position of the harvester, are collected by the system during harvest. The height of the header, the moisture content of grains, and the torque of the header power shaft are selected as the input quantity, and feed rate is selected as the output quantity. A calculation model for feed rate is established using a particle swarm optimization–back propagation neural network, and then real-time monitoring of feed rate is realized. On the basis of the monitoring of feed rate, an estimation method for yield distribution information is proposed. Through analyzing the rice samples in the experimental area, the method establishes a binary linear model between the header height, the grain moisture content, and the stem-to-grain ratio of the rice fed to the harvester. The model is combined with the monitoring results of feed rate to obtain the rice yield data fed to the harvester. Then, the rice yield data are matched with the positional information at the time of harvesting to obtain a yield distribution map of the harvested area. Field experiments are conducted in a rice-growing area in Northeast China, and the average relative error of the feed rate monitoring system is 7.69%. The field sample verification accuracy of the stem-to-grain ratio model is higher than 90%, and the correlation coefficient R2 is 0.93. The estimation accuracy of the yield distribution is higher than 90%, which provides a scientific basis for agricultural decision making.

Aerodynamic Shape Optimization of an Arc-Plate-Shaped Bluff Body via Surrogate Modeling for Wind Energy Harvesting

Galloping-based piezoelectric wind energy harvesters (WEHs) are being used to supply renewable electricity for self-powered devices. This paper investigates the performance of a galloping-based piezoelectric WEH, with different arc-plate-shaped bluff bodies to improve harvesting efficiency. The Latin hypercube sampling method was employed to design the experiment. After conducting a series of wind tunnel tests, a Kriging surrogate model was then established, with high accuracy. The results show that the wind energy harvester with an arc angle 0.40π and tail length 1.26D generated the maximum power. The output power of the proposed WEH was doubled by optimizing the aerodynamic shape of the bluff body. The reasons for the improvement are discussed in detail. The force measurement results indicated that a large value of the transverse force coefficient means a large galloping response of the WEH. The aerodynamic optimization of this study can be applied to improve the performance of galloping-based wind energy harvesters.

Improve harvesting efficiency of tri-stable energy harvester by tailoring potential energy

Improve harvesting efficiency of tri-stable energy harvester by tailoring potential energy

Tissue Structure Changes of Aquilaria sinensis Xylem after Fungus Induction

In this study, we analyzed the mechanism and the process of fungal-induced agarwood formation in Aquilaria sinensis and studied the functional changes in the xylem structure after the process. The microscopic structure of the white zone, transition zone, agarwood zone, and decay zone of 12-and 18-months of inoculation A. sinensis xylem was studied. The distribution of nuclei, starch grains, soluble sugars, sesquiterpenes, fungal propagules, and mycelium in xylem tissues was investigated by histochemical analysis. The results show that the process of agarwood formation was accompanied by apoptosis of parenchyma cells such as interxylary phloem, xylem rays, and axial parenchyma. Regular changes in the conversion of starch grains to soluble sugars, the production of sesquiterpenoids, and other characteristic components of agarwood in various types of parenchyma cells were also observed. The material transformation was concentrated in the interxylary phloem, providing a structural and material basis for the formation of agarwood. It is the core part of the production of sesquiterpenoids and other characteristic products of agarwood. Compared with the A. sinensis inoculated for 12 months, the xylem of the A. sinensis inoculated for 18 months was more vigorous. There were no significant differences between the 12 and 18 months of inoculation in terms of sugars and agarwood characteristic products. In production, harvesting after 12 months of inoculation can improve harvesting efficiency.

Developing a guiding and growth status monitoring system for riding-type tea plucking machine using fully convolutional networks

Tea (Camellia sinensis) is one of the most popular beverage crops worldwide. As tea production is labor-intensive, riding-type tea plucking machines have been employed to improve harvest efficiency. High level of operating skills are essential to maneuver these machines. Improper operation may cause damage to tea plants and reduce the quality of harvested tea leaves. Besides harvest, monitoring the growth status of tea canopies is another essential task of tea plantation management. Monitoring conducted through conventional manual observation is time consuming. Therefore, this study attempted to solve these two issues by developing a system that guided an operator to steer a riding-type tea plucking machine in real-time and provided growth status of tea plants after each scouting trip. Fully convolutional networks (FCNs) and image processing techniques were applied to develop the guiding subsystem. This study compared two FCNs, namely FCN-8 s and ENet. The results showed that ENet outperformed FCN-8 s in generating a guiding line for the operator. Statistical analysis indicated that, with the guiding subsystem, the deviation between the driving direction and tea row center was significantly reduced. The guiding subsystem also could detect obstacles on the tea row where it was operated on. When in operation, the riding-type tea plucking machine was used as a platform to collect images of tea canopies for tea plantation management. The monitoring subsystem provided a graphical user interface consisting of a route recording, a growth status map, a canopy image of tea plants, and growth status ratings of the tea plants. FCN-8 s performed better than ENet in quantifying the growth status of tea plants, with a mean IU of 0.964 and a mean accuracy of 0.989.