Labor Requirements Research Articles

Methodology For Extracting Poplar Planted Fields From Very High-Resolution Imagery Using Object-Based Image Analysis and Feature Selection Strategy

Abstract. Poplars (Populus sp.), a tree that grows rapidly species, are significant as industrial forest products. The delineation and monitoring of poplar cultivated areas are invaluable for decision-making processes. With the remote sensing technology, accurate detection of poplar planted areas could be determined much faster, more economically, and with minimum labor requirements. The objective of this research is to create a map of poplar plantations in the Sakarya region of Turkey utilizing Worldview-3 satellite imagery. Object-based image analysis (OBIA) through the application of the multi-resolution segmentation method (MRS) was employed to generate image segments, and then three prevailing machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF) and Rotation Forest (RotFor) were implemented to produce LULC maps of the study area including 11 landscape features. The most effective and contributing object features that assure high separability between landscape features were determined using a filter-based Chi-square algorithm for the prediction models constructed with SVM, RF, and RotFor classifiers. Results revealed that the SVM classifier achieved the highest overall accuracy (91.73%) with 38 features out of 88 features, about 3% improvement compared to the other algorithms. According to the SHAP analysis, the IHS feature was the most effective one in the constructed RF model, followed by the CI (red edge), NDVI-1 and NDVI-2 vegetation indices.

Optimizing Bt Cotton Yields with Canopy Management in Rainfed Environments

Cotton, the ‘White gold’ is an important commercial crop in India and central India in particular. Sustainable yield can be achieved by increasing plant density and canopy management. Hence an experiment was conducted at Cotton Research Station, Nanded, (Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, M.S., India) during kharif 2023 on medium black cotton soil in randomized block design. The field trail was conducted with three replications and seven treatments viz.,T1 : Cotton in HDPS - 90 x 30 cm (37,037 plants ha-1), T2 : HDPS + De-topping at 90 DAS, T3 : HDPS + De-topping at 75 DAS, T4 : HDPS + Pruning of monopodia at square formation stage, T5 : HDPS + Pruning of monopodia at square formation stage and de-topping at 75 DAS, T6 : HDPS + two sprays of Mepiquat chloride @ 25 g a.i. at square formation followed by 15 days and T7: HDPS + Polymulch + pruning of monopodia at square formation stage and de-topping at 75 DAS. The results showed that growing of cotton with polythene mulch along with pruning of monopodia at square formation stage and de-topping at 75 DAS treatment recorded highest plant height (109.80 cm), no. of sympodia plant-1 (16.53), leaf area (58.16 dm2 and 109.99 dm2 at 60 and 90 DAS, respectively), leaf area index at 120 DAS (0.2107), seed cotton yield (2734 kg ha-1) and GMR (` 1,87,767 ha-1) as compared to all other treatments. Seed cotton yield was increased by 48.66 per cent, significantly over HDPS planting (90 x 30 cm spacing). While treatment HDPS + pruning of monopodia at square formation stage and de-topping at 75 DAS recorded highest NMR (` 48,896 ha-1) and HDPS + de-topping at 90 DAS recorded highest B:C ratio (1.60) as compared to all other treatments respectively. Treatment HDPS + poly mulch + pruning of monopodia and de-topping recorded highest labour requirement per hectare (112.8 man days) as well as seed cotton yield per labour unit (24.24 kg). Thus, adoption of canopy management practice viz., poly mulch, monopodia pruning, de-topping or use f PGR can improve seed cotton yield with high density planting in rainfed areas where it is very low.

Real-Time Monitoring and Control System Enhances Drilling-Fluid Management

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216322, “Next-Generation Drilling-Fluid Management: Real-Time Monitoring and Autonomous Control,” by Zhicai Zhang, Rached M. Rached, SPE, and Yunbo Xun, Sinopec, et al. The paper has not been peer reviewed. _ Accurate measurement and control of drilling-fluid properties are crucial for safe and successful drilling operations. Traditional manual methods are slow and require significant manpower, underscoring the need for real-time monitoring. This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance. The online monitoring system improves drilling-industry safety and success by measuring properties of drilling fluids in an accurate, timely, and automated matter. Introduction The primary objectives of this project are to develop an innovative online system for monitoring drilling fluids with a focus on facilitating intelligent control of drilling-fluid performance. The system aims at measuring 10 key parameters continuously, including apparent viscosity, plastic viscosity, yield point, density, pH, and ion concentrations. By incorporating online, real-time, continuous monitoring capabilities, the system offers several advantages, including improved data quality and frequency, reduced on-site labor requirements, and a corresponding decrease in associated health and safety hazards. System Description Overview of the Online Drilling-Fluid-Monitoring System. The online drilling-fluid-monitoring system is available in four distinct configurations: land modular, land skid-mounted, offshore cabin, and indoor small modular, providing customization options to meet specific space requirements. The system is designed to measure 10 crucial parameters essential for drilling operations. These include rheology factors, density, pH, sulfur ion content, and chloride ion content. Density and rheology measurements are performed at a 1-Hz sampling rate, offering near-continuous monitoring. The system is capable of operating within an ambient temperature range from –35 to 50°C, and the testing temperature can be adjusted between 4 and 80°C. Enabled by real-time data transmission, the system allows for the online, real-time monitoring of drilling-fluid properties. The system has proven its utility extensively, with more than 30 sets having been deployed successfully across more than 200 wells. Ideally, the monitoring system is positioned near the circulation tank to enable efficient fluid transfer (Fig. 1a). When space near the circulation tank is limited, an alternative placement can be on the opposite side of the blowout-prevention pipeline (Fig. 1b). However, this alternative may lead to a slight delay in data because of extended circulation times.

Fabrication of Remotely Controllable Robotic Weed-Ejector Vehicle

Weed is an unwanted plant in an agriculture field that keeps the crop plants deprived of sunlight, fertilisers and water. If not removed, weed reduces the crop output to a larger extent i.e., endangering the farmer's interests. There are three main reasons for leaving weed to grow in fields and substantial loss of crop output 1) non-availability of agricultural labour, 2) fear of snake bites and 3) higher amounts of time required for weed injection. As of there are no successful ROVs (Remotely operating vehicles) to get to the weed and capable of pulling off the weed without the farmer physically entering into the agricultural fields. The Proposed model reduces farmers getting exposed to snake bites during weed ejection and reduces the time and labour requirement for weed ejection. The project involves the study of different mechanisms required for weed pull-off from the agricultural fields and remote-control systems, modelling of components, fabrication of components, assembly and testing of the Remotely Controllable Robotic Weed-Ejector Vehicle.

Impact of an enterprise controlled substance management system on labor and inventory costs.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To evaluate the impact of an enterprise controlled substance (ECS) management system with integration of analytics software on labor requirements and inventory cost within a health-system pharmacy. A prospective, pre-post observational study was designed to assess the impact of implementing a solution that connects disparate systems with the integration of analytics software. Three study modules were implemented over approximately 18 months. The intervention consisted of implementation of new CS vaults, a centralized server, an automated central medication inventory system, and inventory optimization analytics software. The number of transactions and time spent on CS reports were compared before and after implementation to determine labor and inventory efficiencies. Both of the study facilities had a decrease in CS daily stockouts and an increase in inventory turns compared to baseline, while the total number of transactions (vends) at the central vault and decentralized dispensing cabinets increased. The addition of analytics allowed for establishment of informed changes to periodic automated replenishment levels. Additionally, both facilities saw a reduction in the number of expired medications, and there was subsequently a reduction in the total reverse distributor costs. Finally, both facilities had a reduction in the amount of time spent on manual tasks associated with reconciling and managing discrepancies. An inventory management system integrated with an advanced analytics tool provided a reduction in the time spent on receiving, storing, and reconciling CS records while the number of transactions increased. The ECS solution enhanced the visibility of the chain of custody while closing the loop between reporting and receiving inventory, eliminating or reducing the frequency of manual processes.

The Proliferation of Artificial Intelligence in the Forklift Industry—An Analysis for the Case of Romania

This paper investigates the impact of artificial intelligence (AI) on the forklift industry, focusing on logistics and procurement within small and medium-sized enterprises (SMEs) in Romania. Using a mixed-methods approach, including interviews with seven managers from a benchmarked company in the forklift industry (BCFI) and quantitative analysis of operational data, we examine the transformative effects of AI integration. Key findings include a 30% reduction in inventory holding costs due to AI-powered predictive analytics; a 15% decrease in procurement costs through AI-driven supplier evaluation systems; a 25% increase in operational efficiency from AI-optimized route planning; a 40% boost in overall productivity attributed to AI-enabled automation; and a projected 20% reduction in low-skilled labor requirements over the next five years. The study employs environmental, social, and corporate governance (ESG), balanced scorecard (BSC), benchmarking, and activity-based management (ABM) models to analyze risks and implications of AI integration. A case study of a leading Romanian SME in the forklift industry is presented, examining financial strategies using McKinsey’s 7S framework. The paper concludes that while AI offers significant operational benefits, it also presents challenges in workforce transition and ethical considerations that require careful management.

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

IoT- enabled crop waste mulching machine for sustainable farming: perspective of circular economy

Abstract A large amount of crop waste is generated after crop harvesting. A substantial quantity of waste on the farm is burnt to clear the field, contributing to environmental harm and global warming. The study aims to develop a technological solution for crop waste management (CWM) by designing and developing an IoT-enabled crop waste mulching machine. This initiative addresses the environmental and health issues caused by burning crop waste on farms. The research involves designing and developing an IoT-enabled mulching machine that can be attached to the back side of a tractor. The machine consists of power-transmitting elements, supports, and mechanisms to distribute mulching material evenly onto the plant bed. It incorporates advanced IoT load cell sensors, soil moisture sensors, and proximity sensors to optimize the mulching process based on soil moisture content and to prevent machinery blockages. The IoT-enabled mulching machine effectively manages crop waste by utilizing it in mulch, thus preventing the harmful practice of burning waste. This machine ensures a more efficient, safer, and environmentally friendly approach to mulching, offering significant improvements over traditional manual mulching methods.
The novelty of this research lies in integrating IoT technologies within mulching machine. The mulching machine uses sensors to automate and optimize the mulching process, representing a significant technological advancement over conventional methods. The study offers practical benefits by reducing labour requirements, minimizing the risk of injuries, and ensuring a more uniform mulch application. This technology facilitates more sustainable farming practices, aligning with global sustainability goals. The study can potentially revolutionize the agricultural equipment manufacturing industry (AEMI) by shifting the focus toward environmental sustainability. It promotes replacing plastic film mulching machines with more eco-friendly crop waste mulching solutions. This technology has the potential to reduce air pollution, carbon emissions, and mitigate climate. It also enhances soil health through better mulching practices.

Why mow?: A review of the resulting ecosystem services and disservices from mowing turfgrass

AbstractTurfgrasses are those grasses that tolerate frequent mowing. The act of mowing turfgrasses, primarily lawn mowing, has received much negative attention primarily due to its labor requirement and the resulting mower emissions. This paper provides a comprehensive review of the benefits and drawbacks of mowing grasses, specifically turfgrasses used on lawns, parks, golf courses, and sports fields, using an ecosystem services and disservices framework. Discussed is the challenge of creating a “one‐size‐fits‐all” approach to selecting the best mowing management practices and sociocultural tensions that exist related to lawn mowing. Discussion also includes the benefits and value gained from mowing turfgrass and strategies to mitigate ecosystem disservices that result from mowing.

Optimal Layout of Modular Systems by Geometric Perturbations

In this study, we investigate the potential of integrating geometric perturbations into the design process to optimise modular systems such as floor coverings, façade claddings, and masonry walls. By allowing small geometry adjustments of the initial design, we achieve significant reductions in material waste or labour requirements, leading to cost-saving and environmental benefits without compromising the design concept. We compared our approach to traditional methods, where the layout of the modular system is determined after the geometry of the design is finalised. To illustrate our method, we present case studies for two- and three-dimensional modular designs.

Assessing the Efficiency and Economic Feasibility of Mustard Cultivation Using a Happy Seeder

Mustard, scientifically known as Brassica juncea, is a vital oilseed crop sown during the Rabi season in various agricultural regions. This crop not only contributes significantly to the edible oil supply but also plays a crucial role in the agricultural economy, providing farmers with essential income and employment opportunities. However, one of the major challenges faced by farmers in mustard cultivation is the tillage process, which is both time-consuming and labor-intensive. Conventional tillage methods often require extensive land preparation, leading to increased labor costs and decreased efficiency. In light of these challenges, this study was conducted to evaluate the efficacy of the Happy Seeder technology, a modern innovation designed to facilitate direct sowing in the standing stubble of previous crops. This research focused on the mustard variety Pusa-27 and was executed over two consecutive Rabi seasons, specifically from 2021-22 and 2022-23. The experiment was implemented by KVK Rohtas, utilizing both long-term experimental plots and 20 different farmers’ fields to ensure a comprehensive assessment across various agricultural settings. To analyze the impact of the two different sowing methods, two groups of plots were established at each site: one group employed the conventional tillage method (T1), while the other group utilized the Happy Seeder technique (T2). The experimental design followed a randomized block format, consisting of 21 replications for each treatment to ensure robust and reliable data collection. The results of the study revealed that the Happy Seeder sowing method significantly enhanced the productivity of mustard. The average yield recorded for the Happy Seeder group was an impressive 16.25 quintals per hectare, markedly higher than the 12.70 quintals per hectare achieved with conventional tillage. This increase in yield not only highlights the effectiveness of the Happy Seeder technology but also suggests that it can potentially help meet the growing demand for oilseeds. In addition to higher yields, the Happy Seeder method also resulted in reduced input costs, primarily due to lower labor requirements associated with its use. The reduced need for extensive tillage and the ability to sow directly into the stubble allow farmers to save on labor costs and time, making the farming process more efficient. Moreover, the implementation of the Happy Seeder method was associated with significant improvements in soil health. The study indicated enhancements in both the physico-chemical and biological properties of the soil, which are crucial for sustainable agricultural practices. Improved soil health not only contributes to better crop yields but also promotes long-term fertility, allowing farmers to sustain their agricultural productivity over time. This result is in conformity to Jat et al. 2009. In conclusion, the findings of this study strongly advocate for the adoption of the Happy Seeder sowing method in mustard cultivation. The notable improvements in productivity, reduction in input costs, and enhancement of soil health suggest that this innovative technique can play a pivotal role in transforming mustard farming into a more sustainable and profitable venture. By embracing such modern agricultural practices, farmers can significantly contribute to food security while also improving their economic viability.

Importance-Induced Customer Segmentation Using Explainable Machine Learning

Abstract Customer segmentation plays a critical role in enhancing a company's product penetration rate in the market. It enables numerous downstream applications such as customer-oriented product development and trend analysis. Previous approaches to customer segmentation have relied either on survey-based methods or data-driven approaches. However, these methods face challenges such as high human labor requirements or the generation of noisy segments. To address these challenges, this paper proposes a new methodology based on data-driven network construction and an importance-enhanced framework. The framework incorporates two techniques: (1) the utilization of a neural network model to compute feature importance values and (2) the proposal of a novel network connection rule. This framework addresses the limitation of the previous approach, sentimentpolarity- based networking, by connecting customers based on feature importance. We further validated the effectiveness of the framework using three real-world datasets and demonstrated the proposed method outperformed the previous approach.

Weed management options in drum seeded wetland rice

High labour requirement and cost for raising nursery and transplanting of the seedlings has necessitated the shift from transplanted rice to direct-seeded rice. To reduce the requirement of large quantity of seeds, incidence of pests and diseases in broadcasting and to ensure uniform seed distribution for consistent crop growth and easier maintenance, mechanization is needed in direct-seeded rice. The major constraint experienced in wet DSR is the occurrence of high weed infestation as emerging seedlings in direct-sown fields are less competitive with concurrently emerging weeds. An experiment was conducted at M. S. Swaminathan Rice Research Station, Moncompu, Kerala Agricultural University during kharif and rabi 2021-22, with eight treatments and three replications in randomised complete block design to identify suitable cost-effective agronomic management practices to enhance the productivity of drum seeded rice and to maximise its resource-use-efficiency. The results revealed wet DSR using drumseeder as a viable crop establishment method for reducing the seed requirement, cultivation time, energy, cost of cultivation, pest and disease infestation, minimise plant stress due to transplanting and sustaining yield. There was significant variation in the yield and yield attributes in drum seeding depending on the weed management strategies adopted viz., two conoweedings, post emergent herbicide application, pre followed by post emergent herbicide application, post emergent herbicide application followed by conoweeding. Post-emergence application of Chlorimuron ethyl + Metsulfuron methyl @ 0.004 kg/ha and Penoxsulam + Cyhalofop butyl @ 0.135 kg/ha as tank mix at 18 DAS followed by conoweeding at 30 DAS was found economical in drum seeding which was on par yield with broadcasted crop yield under post emergent herbicide application.

Evaluation of unmanned aerial vehicle for effective spraying application in coconut plantations

Unmanned aerial vehicle (UAV) pesticide application in recent years owing to its importance such as time saving, reduction in human drudgery and also reduction in pesticides application rate. UAV has a great potential to address the problem involved in manual chemicals spraying in tall crops like coconut plantation where at present operation performed by manual climbing involves lots of drudgery and life risk. The current study aimed to understand the most influencing spraying parameters, such as spray height and spray time of the UAV sprayer on droplet characteristics such as spray droplet size, spray coverage and spray deposition at different layers (spindle, middle and bottom) of coconut tree canopy. The selected spray height (1, 2 and 3 m) and spray time (5, 8 and 11 s) significantly affects (p < 0.05) the droplet size (μm), spray coverage (%) and spray deposition (μl cm−2). In spray droplet size, the treatment T4, T5, T7 and T8 were recorded recommended droplet size of 50–400 μm in all layer of the coconut tree canopy. In spray coverage, the nearest value for recommended spray coverage of 10–20 % was observed for T1 and T5 treatment in all layer of the coconut tree canopy. The maximum penetration efficiency of 34.41 % had achieved at spray height of 2m and spray time of 8s (treatment T5). Based on performance of selected parameter, the spray height of 2 m and spray time of 8 s (treatment T5) was found best for spraying operation using UAV in coconut tree. The results showed the performance of the UAV offers best alternative for spraying operation on coconut tree and also this system will drastically reduce application time, labour requirement and improved the safety of coconut farmers.

Development and evaluation of a self-adaptable planting unit for an autonomous planting process of field vegetables

Today, the number of solutions for automated processes in agriculture is growing rapidly. This is primarily driven by the lack of available and affordable labour, pricing pressures, and regulatory requirements. Vegetable production in particular has a lot of potential for automation, as many process steps, such as planting, are performed partly manually. Fully automated systems for the planting process are characterized by their big size, which is only suitable for large farms. At the same time, these planters typically have a low level of intelligence, which is essential for a fully autonomous planting process performed by autonomous vehicles or robots. The following work therefore deals with the development and construction of a prototype for vegetable planting via a robotic platform. This prototype is designed to meet the requirements of a conventional planter and carry out the planting process automatically using a robotic platform. To ensure a robust robotic planting process, an AI-based control system has been integrated that can detect and adjust the planting quality. For this reason, the planting unit was designed to allow dynamic changes in working depth and furrow width. By dynamically controlling these planting parameters, there is potential for a more sustainable planting process with lower energy requirements. A number of evaluations have been carried out to validate the described characteristics of the prototype planting unit.

Comparative Study on Cost Analysis of Mechanical and Manual Transplanting of Rice

In Telangana, rice cultivation predominantly relies on traditional manual transplanting, which requires a significant labor force. However, the migration of agricultural laborers to urban areas in search of better wages has led to a shortage of workers, particularly during the critical transplanting season. To address this labor shortage and reduce cultivation costs, an efficient and cost-effective method of rice transplanting is essential, without compromising grain yield. To explore such a solution, Krishi Vigyan Kendra in Jammikunta, Karimnagar district, conducted 30 frontline demonstrations between 2018-19 and 2020-21, showcasing rice cultivation using a self-propelled, walk-behind six-row mechanical transplanter. The performance of this mechanical method was evaluated by collecting data on various growth parameters, including plant height, number of productive tillers per hill, panicle length, grains per panicle, and grain yield, and comparing it with traditional manual transplanting. Economic analysis revealed that the gross returns from mechanical transplanting were Rs. 135,956, compared to Rs. 128,629 for manual transplanting. Similarly, the net returns for mechanical and manual methods were Rs. 95,106 and Rs. 82,204, respectively. The benefit-cost ratio was found to be 3.32 for mechanical transplanting and 2.77 for manual transplanting. These findings indicate that mechanical rice transplanting offers a viable, cost-effective alternative, reducing labor requirements and drudgery while delivering higher yields.

The role of Social Education in the basic technical requirements of labour

The role of Social Education in the basic technical requirements of labour

Identification of Insertion and Deletion (InDel) Markers for Chickpea (Cicer arietinum L.) Based on Double-Digest Restriction Site-Associated DNA Sequencing.

Enhancing the marker repository and the development of breeder-friendly markers in chickpeas is important in relation to chickpea genomics-assisted breeding applications. Insertion-deletion (InDel) markers are widely distributed across genomes and easily observed with specifically designed primers, leading to less time, cost, and labor requirements. In light of this, the present study focused on the identification and development of InDel markers through the use of double-digest restriction site-associated DNA sequencing (ddRADSeq) data from 20 chickpea accessions. Bioinformatic analysis identified 20,700 InDel sites, including 15,031 (72.61%) deletions and 5669 (27.39%) insertions, among the chickpea accessions. The InDel markers ranged from 1 to 25 bp in length, while single-nucleotide-length InDel markers were found to represent the majority of the InDel sites and account for 79% of the total InDel markers. However, we focused on InDel markers wherein the length was greater than a single nucleotide to avoid any read or alignment errors. Among all of the InDel markers, 96.1% were less than 10 bp, 3.6% were between 10 and 20 bp, and 0.3% were more than 20 bp in length. We examined the InDel markers that were 10 bp and longer for the development of InDel markers based on a consideration of the genomic distribution and low-cost genotyping with agarose gels. A total of 29 InDel regions were selected, and primers were successfully designed to evaluate their efficiency. Annotation analysis of the InDel markers revealed them to be found with the highest frequency in the intergenic regions (82.76%), followed by the introns (6.90%), coding sequences (6.90%), and exons (3.45%). Genetic diversity analysis demonstrated that the polymorphic information content of the markers varied from 0.09 to 0.37, with an average of 0.20. Taken together, these results showed the efficiency of InDel marker development for chickpea genetic and genomic studies using the ddRADSeq method. The identified markers might prove valuable for chickpea breeders.

Deep learning approach for detecting tomato flowers and buds in greenhouses on 3P2R gantry robot

In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and robotics for pollinating flowers. Robotic pollination offers several benefits, including reduced labor requirements and preservation of costly pollen through artificial tomato pollination. However, previous studies have primarily focused on the labeling and detection of tomato flowers alone. Therefore, the objective of this study was to develop a comprehensive methodology for simultaneously labeling, training, and detecting tomato flowers specifically tailored for robotic pollination. To achieve this, transfer learning techniques were employed using well-known models, namely YOLOv5 and the recently introduced YOLOv8, for tomato flower detection. The performance of both models was evaluated using the same image dataset, and a comparison was made based on their Average Precision (AP) scores to determine the superior model. The results indicated that YOLOv8 achieved a higher mean AP (mAP) of 92.6% in tomato flower and bud detection, outperforming YOLOv5 with 91.2%. Notably, YOLOv8 also demonstrated an inference speed of 0.7 ms when considering an image size of 1920×1080\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1920 \ imes 1080$$\\end{document} pixels resized to 640×640\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$640 \ imes 640$$\\end{document} pixels during detection. The image dataset was acquired during both morning and evening periods to minimize the impact of lighting conditions on the detection model. These findings highlight the potential of YOLOv8 for real-time detection of tomato flowers and buds, enabling further estimation of flower blooming peaks and facilitating robotic pollination. In the context of robotic pollination, the study also focuses on the deployment of the proposed detection model on the 3P2R gantry robot. The study introduces a kinematic model and a modified circuit for the gantry robot. The position-based visual servoing method is employed to approach the detected flower during the pollination process. The effectiveness of the proposed visual servoing approach is validated in both un-clustered and clustered plant environments in the laboratory setting. Additionally, this study provides valuable theoretical and practical insights for specialists in the field of greenhouse systems, particularly in the design of flower detection algorithms using computer vision and its deployment in robotic systems used in greenhouses.

Battery-Operated Power Weeding Machines: Advancements, Challenges, and Future Prospects

In recent years, significant advancements have been made in the development and adoption of battery-operated power weeding machines, offering a sustainable and efficient alternative to traditional weed management methods. Previous studies have highlighted several critical benefits of these machines, particularly in reducing labor costs and minimizing environmental impact. Battery-powered weeders have been shown to significantly cut down on manual labor requirements, thus reducing the physical strain on farmers and decreasing the reliance on hired labor. This reduction in operational costs makes them an economically viable option for small and medium-sized farms. Furthermore, the environmental advantages of these machines are notable. Unlike chemical herbicides, which pose risks of soil and water contamination and potential health hazards, battery-operated weeders offer a clean alternative, eliminating the need for harmful chemicals. This aligns with the growing demand for environmentally sustainable agricultural practices. Technological advancements have further enhanced the efficiency and usability of these machines. Modern battery-powered weeders are increasingly equipped with lithium-ion batteries that provide longer operation times, precision sensors for accurate weed targeting, and automated control systems that simplify operation. These innovations have not only improved the effectiveness of weed management but have also made these machines more accessible and user-friendly for farmers. Field trials and studies conducted in various agricultural settings have consistently demonstrated the practicality and reliability of battery-operated weeders, making them a transformative solution in modern agriculture. The integration of these machines into farming practices has been shown to improve productivity, reduce costs, and promote sustainability, solidifying their role as a critical innovation in the future of agriculture. This review article aims to build upon these findings by providing a detailed examination of the design, development, and performance of battery-operated weeding machines, and exploring their broader applications in diverse agricultural environments.