Precise Technology Research Articles

Research on online detection technology of triangular-blade tool grinding precision based on machine vision

Research on online detection technology of triangular-blade tool grinding precision based on machine vision

Patricia E. Bath: A Luminary in Ophthalmology.

Renowned ophthalmologist and inventor Dr. Patricia Bath made ground-breaking contributions to medicine. After seeing that blindness was more common among Black patients, she focused much of her career on improving access to eye care for underserved communities. Bath's most important contributions to her field of ophthalmology were the inventions of community ophthalmology and the laserphaco probe. Community ophthalmology improves access to eye care by combining treatment, education, and preventive care into a coherent program targeted toward serving underprivileged populations. The laserphaco probe combines laser fragmentation with ultrasonic removal, which greatly improves the accuracy and safety of cataract surgery. Laser technology's precision also reduces the possibility of surgical mistakes, thus increasing success rates and improving the general results. Apart from improving the quality of life for many people, Dr. Bath's inventions established a new benchmark in blindness prevention and cataract therapy, thus guiding modern ophthalmology and ocular surgery.

Financial risk management in agriculture: mitigation strategies and protection against economic variables

This work explores essential strategies for managing financial and operational risks in the agricultural sector, a crucial pillar of the global economy, but one that is exposed to a wide range of financial risks. These risks include fluctuations in commodity prices, access to credit, operational risks, climate change, exchange rate variations, and government policies. To ensure the sustainability and profitability of agricultural operations, it is imperative that producers adopt effective financial risk management strategies. This article addresses the main mitigation and protection strategies against economic variables in the context of agriculture. The methodology of this study is based on a systematic literature review, focused on identifying, analyzing, and synthesizing financial risk management strategies in agriculture. The review covers the analysis of advanced precision technologies, which optimize agricultural production and reduce waste, as well as sustainable agricultural practices, adequate insurance to mitigate the impacts of climate risks, and commodity hedging. Strategies for diversifying crops and agricultural activities to distribute risk and improve financial resilience are also discussed. In conclusion, effective financial risk management in agriculture requires a multifaceted approach that integrates mitigation strategies for various types of risks. The adoption of advanced technologies, sustainable agricultural practices, adequate insurance, and a thorough understanding of market and policy dynamics are fundamental to strengthening resilience and ensuring the sustainability of the agricultural sector against adverse economic variables.

YOLOv7-Branch: A Jujube Leaf Branch Detection Model for Agricultural Robot.

The intelligent harvesting technology for jujube leaf branches presents a novel avenue for enhancing both the quantity and quality of jujube leaf tea, whereas the precise detection technology for jujube leaf branches emerges as a pivotal factor constraining its development. The precise identification and localization of jujube leaf branches using real-time object detection technology are crucial steps toward achieving intelligent harvesting. When integrated into real-world scenarios, issues such as the background noise introduced by tags, occlusions, and variations in jujube leaf morphology constrain the accuracy of detection and the precision of localization. To address these issues, we describe a jujube leaf branch object detection network based on YOLOv7. First, the Polarized Self-Attention module is embedded into the convolutional layer, and the Gather-Excite module is embedded into the concat layer to incorporate spatial information, thus achieving the suppression of irrelevant information such as background noise. Second, we incorporate implicit knowledge into the Efficient Decoupled Head and replace the original detection head, enhancing the network's capability to extract deep features. Third, to address the issue of imbalanced jujube leaf samples, we employ Focal-EIoU as the bounding box loss function to expedite the regression prediction and enhance the localization accuracy of the model's bounding boxes. Experiments show that the precision of our model is 85%, which is increased by 3.5% compared to that of YOLOv7-tiny. The mAP@0.5 value is 83.7%. Our model's recognition rate, recall and mean average precision are superior to those of other models. Our method could provide technical support for yield estimation in the intelligent management of jujube orchards.

Tracking Free-Ranging Pantaneiro Sheep during Extreme Drought in the Pantanal through Precision Technologies

The Pantanal has been facing consecutive years of extreme drought, with an impact on the quantity and quality of available pasture. However, little is known about how locally adapted breeds respond to the distribution of forage resources in this extreme drought scenario. This study aimed to evaluate the movement of free-grazing Pantaneiro sheep using a low-cost GPS to assess the main grazing sites, measure the daily distance traveled, and determine the energy requirements for walking with body weight monitoring. In a herd of 100 animals, 31 were selected for weighing, and six ewes were outfitted with GPS collars. GPS data collected on these animals every 10 m from August 2020 to May 2021 was analyzed using the Python programming language. The traveled distance and activity energy requirements (ACT) for horizontal walking (Mcal/d of NEm) were determined. The 31 ewes were weighed at the beginning and end of each season. The available dry matter (DM) and floristic composition of the grazing sites were estimated at the peak of the drought. DM was predicted using power regression with NDVI (normalized difference vegetation index) (R2 = 0.94). DM estimates averaged 450 kg/ha, ranging from traces to 3830 kg/ha, indicating overall very low values. Individual variation in the frequency of use of grazing sites was observed (p < 0.05), reflecting the distances traveled and the energetic cost of the activity. The range of distances traveled by the animals varied from 3.3 to 17.7 km/d, with an average of 5.9 km/d, indicating low energy for walking. However, the traveled distance and ACT remained consistent over time; there were no significant differences observed between seasons (p > 0.05). On average, the ewes’ initial weight did not differ from the weight at the drought peak (p > 0.05), indicating that they maintained their initial weight, which is important for locally adapted breeds as it confers robustness and resilience. This study also highlighted the importance of the breed’s biodiverse diet during extreme drought, which enabled the selection of forage for energy and nutrient supplementation. The results demonstrated that precision tools such as GPS and satellite imagery enabled the study of animals in extensive systems, thereby contributing to decision-making within the production system.

Optimizing UAV spray parameters to improve precise control of tobacco pests at different growth stages.

Unmanned aerial vehicles (UAVs) for pesticide application show promising potential in tobacco pest management. However, the impact of flight parameters on spray efficacy requires further investigation. Three field experiments were conducted from the rosette to the maturation stage of tobacco to systematically assess spray efficacy under varying flight heights, speeds, and application volumes. Using a multi-index weight analysis method, optimal operational parameter combinations for different tobacco growth stages were evaluated and compared with backpack electric sprayers. For the rosette stage, the recommended parameter is a flight speed of 5 m s-1, a flight height of 2 m, and a liquid application volume of 30 L hm-2; during the vigorous growth stage, the suggested parameter includes a flight speed of 3 m s-1, a flight height of 2 m, and a liquid application volume of 22.5 L hm-2. In the maturing stage, optimal parameter consists of a flight speed of 3 m s-1, a flight height of 3.5 m, and a liquid application volume of 30 L hm-2. Furthermore, UAV spraying achieves higher droplet deposition on both sides of tobacco leaves compared to traditional electric backpack sprayers. Adjusting UAV spraying parameters for different tobacco growth stages is crucial. These results can provide the methods for the precise control technology of tobacco pests at different growth stages. © 2024 Society of Chemical Industry.

A Light-Powered Single-Stranded DNA Molecular Motor with Colour-Selective Single-Step Control.

Top-down control of small motion is possible through top-down controlled molecular motors in replacement of larger actuators like MEMS or NEMS (micro- or nano-electromechanical systems) in the current precision technology. Improving top-down control of molecular motors to every single step is desirable for this purpose, and also for synchronization of motor actions for amplified effects. Here we report a designed single-stranded DNA molecular motor powered by alternated ultraviolet and visible light for processive track-walking, with the two light colours each locking the motor in a full directional step to allow saturated driving but no overstepping. This novel nano-optomechanical driving mechanism pushes the top-down control of molecular motors down to every single step, thus providing a key technical capability to advance the molecular motor-based precision technology and also motor synchronization for amplified effects.

A Light‐Powered Single‐Stranded DNA Molecular Motor with Colour‐Selective Single‐Step Control

AbstractTop‐down control of small motion is possible through top‐down controlled molecular motors in replacement of larger actuators like MEMS or NEMS (micro‐ or nano‐electromechanical systems) in the current precision technology. Improving top‐down control of molecular motors to every single step is desirable for this purpose, and also for synchronization of motor actions for amplified effects. Here we report a designed single‐stranded DNA molecular motor powered by alternated ultraviolet and visible light for processive track‐walking, with the two light colours each locking the motor in a full directional step to allow saturated driving but no overstepping. This novel nano‐optomechanical driving mechanism pushes the top‐down control of molecular motors down to every single step, thus providing a key technical capability to advance the molecular motor‐based precision technology and also motor synchronization for amplified effects.

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer.

Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

Temporal metabolic dynamics and heterogeneity of viable but nonculturable Pseudomonas aeruginosa induced by chlorine disinfection at single-cell resolution

Characterizing the metabolic network and dynamics of Viable But Nonculturable (VBNC) bacteria is crucial for their identification and control. In this study, single-cell Raman spectroscopy was used to explore the metabolic intricacies of VBNC P. aeruginosa induced by chlorine disinfection over 24 h. VBNC P. aeruginosa exhibited significant reductions of 75.98 %, 84.26 % and 81.07 % in carbohydrate, protein and nucleic acid Raman peaks, signaling decreased metabolic activity in these crucial components. Intriguingly, lipid metabolism in VBNC P. aeruginosa remained robust, indicating a potential adaptation to environmental stress. Furthermore, this study analyzed Raman characteristic peaks to examine metabolic conversion in these bacteria. VBNC P. aeruginosa displayed distinct phase-specific metabolic conversion patterns compared to culturable ones. These findings shed light on the temporal dynamics of metabolic processes, such as lipid-carbohydrate conversion, which appeared to be ongoing throughout the incubation period in VBNC P. aeruginosa. Compared to the culturable state, VBNC P. aeruginosa exhibited reduced isotopic assimilation rates for carbon, nitrogen and hydrogen by 40.57 %, 44.62 % and 39.45 %, indicating decreased substrate uptake for biomolecular synthesis. Additionally, the metabolic heterogeneity index of the VBNC bacterial population increased by 37.28 %, with this heterogeneity displaying a progressive increase with the duration of the culture. This observation suggests VBNC P. aeruginosa differentiation into distinct subpopulations, believed crucial for survival in challenging environments. These findings reveal the metabolic dynamics of VBNC state bacteria, providing insights into their adaptive strategies and implications for environmental health risk assessment and precise control technologies.

“Crossbreeding” NIR‐II flavchromene for PSMA‐positive prostate cancer detection and image‐guided surgery

AbstractProstate‐specific membrane antigen (PSMA) is known to be overexpressed in prostate cancer (PCa). The development of precise and rapid imaging technologies to monitor PSMA is crucial for early diagnosis and therapy. Fluorescence imaging in the second near‐infrared window (NIR‐II) has emerged as a powerful tool for real‐time tracking and in vivo visualization, offering high sensitivity and resolution. However, there is a lack of stable, bright and easy‐to‐implement NIR‐II fluorescent probes for PSMA targeting. Herein, we presented a PSMA‐targeting NIR‐II fluorescent probe FC‐PSMA based on π‐conjugated crossbreeding dyed strategy that affords high stability, large extinction coefficient, and good brightness. As demonstrated, FC‐PSMA displayed a high fluorescence quantum yield in fetal bovine serum (FBS). Following intravenous injection of FC‐PSMA, the tumor‐to‐normal ratio of fluorescence intensity steadily increased over time, reaching a peak at 48 h (tumor‐to‐leg ratio = 12.16 ± 0.90). This advancement enables precise identification of PC through NIR‐II fluorescence imaging, facilitating high‐performance guidance for prostate cancer resection surgery.

Insights into Prime Editing Technology: A Deep Dive into Fundamentals, Potentials, and Challenges.

As the most versatile and precise gene editing technology, prime editing (PE) can establish a durable cure for most human genetic disorders. Several generations of PE have been developed based on an editor machine or prime editing guide RNA (pegRNA) to achieve any kind of genetic correction. However, due to the early stage of development, PE complex elements need to be optimized for more efficient editing. Smart optimization of editor proteins as well as pegRNA has been contemplated by many researchers, but the universal PE machine's current shortcomings remain to be solved. The modification of PE elements, fine-tuning of the host genes, manipulation of epigenetics, and blockage of immune responses could be used to reach more efficient PE. Moreover, the host factors involved in the PE process, such as repair and innate immune system genes, have not been determined, and PE cell context dependency is still poorly understood. Regarding the large size of the PE elements, delivery is a significant challenge and the development of a universal viral or nonviral platform is still far from complete. PE versions with shortened variants of reverse transcriptase are still too large to fit in common viral vectors. Overall, PE faces challenges in optimization for efficiency, high context dependency during the cell cycling, and delivery due to the large size of elements. In addition, immune responses, unpredictability of outcomes, and off-target effects further limit its application, making it essential to address these issues for broader use in nonpersonalized gene editing. Besides, due to the limited number of suitable animal models and computational modeling, the prediction of the PE process remains challenging. In this review, the fundamentals of PE, including generations, potential, optimization, delivery, invivo barriers, and the future landscape of the technology are discussed.

Practical use of Agriculture 4.0 digital technologies to meet the EU's strategic goals in Czech agriculture

The implementation of precision technology procedures is declared in EU documents, including the implementation of these technologies in all areas of production, production, trade, including all areas of everyday life of the population. The article deals with the use of IT technologies in activities dealing with land management: i.e. crop production including landscape maintenance. In this context, precision technologies are used in agriculture, crop and livestock production, horticulture, forestry and comprehensive maintenance of the agricultural landscape. With remote sensing, all land areas are monitored, data is stored, and the whole process takes place continuously. The data is displayed in information maps that perfectly map the properties of the monitored area. Today, the development of the information society and specialized internet services of the state administration would not be possible without precise technologies and information portals. These are portals for the public needs of citizens or a specific professional community, and these must be user-friendly, clear, intuitive, visually interesting, trustworthy and complete in content. The article deals with a detailed analysis of the Czech Farmer's Portal from multiple angles, including state-of-the-art tools such as the AMS system (AMS, 2023). This is a new way of monitoring agricultural areas using a satellite system. On the basis of the analysed services, deficiencies are identified and suggestions for improvement are presented.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

Research and application of continuous and precise nitrogen injection fire extinguishing technology in coal mines

To overcome shortcomings in traditional nitrogen injection fire prevention techniques, such as discontinuous nitrogen distribution, poor inerting effects, and severe waste of pipeline resources, a method for continuous and precise nitrogen injection fire extinguishing was proposed. To implement this method, this study utilizes FLUENT, FLAC 3D simulations, and principles of elasticity to determine the process and dynamic parameters of nitrogen injection. This information guides the selection, design, and manufacturing of equipment components, leading to the development of a novel “rotating towing type” continuous and precise nitrogen injection fire extinguishing equipment. Taking Wenzhuang 15,102 working face as an example, the inerting effect was compared and analyzed by using CO, Graham coefficient R2 (ΔCO/ΔO2), R3 (ΔCO/ΔCO2) and other quantitative indicators. The findings revealed following results: after deploying this equipment, there was an approximate 70 % reduction in CO concentration. Comparatively, R2 in the extraction area decreased by 45 % compared to the 0.38 % observed in the buried pipe nitrogen injection process. Similarly, the R3 values for the gob and upper corner were reduced by about 56 % and 60 %, respectively. These substantial improvements highlight the superior efficacy of the continuous and precise nitrogen injection fire extinguishing method for nitrogen injection and fire suppression in gobs.

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 μL of serum obtained at the same time was added onto the Au–ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy; however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.

Selective and Sensitive Detection of Fe3+ Ions Using a Red-Emissive Fluorescent Probe Based on Triphenylamine and Perylene-Linked Conjugated Microporous Polymer.

The Expansion of modern industry underscores the urgent need to address heavy metal pollution, which is a threat to human-health and environment. Efforts are underwent to develop precise technologies for detecting heavy metal ions (M+-ion). One promising approach involves the use of Conjugated Microporous Polymers (CMPs) modified with Triphenylamine (TPA) anderylene (Peryl), known as TPA-Peryl-CMP, which emits strong refluorescence. Various analytical techniques, such as Brunauer-Emmett-Teller analysis, Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and thermogravimetric analysis (TGA), are utilized to characterize the synthesized TPA-Peryl-CMP and understand its functional properties. In addition to its remarkable fluorescence behavior, TPA-Peryl-CMP shows promise as a sensor for Fe3+ ions using a turn-off strategy. Due to its exceptional stability and robust π-electron system, this platform demonstrates remarkable sensitivity and selectivity, significantly improving detection capabilities for specific analytes. Detailed procedures related to the mechanism for detecting Fe3+ ions are outlined for sensing Fe3+ ions, revealing a notably strong linear correlation within the concentration range of 0-3µM, with a correlation coefficient of 0.9936 and the Limit of detection (LOD) 20nM. It is anticipated that development of such a kind of TPA-Peryl-CMP will observe broader applications in detecting various analytes related to environmental and biological systems.

Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis.

The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are "Computers and Electronics in Agriculture" and "Journal of Dairy Science". It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.

Assessing risk of acute respiratory infectious diseases in crowded indoor settings with digital twin and precision trajectory approach

Indoor environments can pose a substantial respiratory infection risk, especially in densely populated areas. It is therefore vital to evaluate and predict infection risks for mobile individuals engaging in indoor activities. However, previous studies have primarily focused on assessing viral transmission probabilities between relatively stationary individuals, disregarding the dynamic features of pedestrian trajectories. To address this issue, we propose an approach that uses precise trajectory data and digital twin technology to evaluate and forecast indoor infection risks for mobile individuals. Our approach involves the utilization of high-precision trajectory data to develop a time-varying virus density field map (VDFM), forming the basis for infection risk assessment. Additionally, we present a deep-learning model that employs the transformer method to predict future time-varying VDFMs and associated infection risks. Furthermore, we introduce digital twin to comprehend real-time interactions between the physical and digital realms within the structure. To validate our approach, we conducted a case study at the Wuhan International Exposition Center, serving as the control room. We performed a sensitivity analysis on various preventive measures, including mask-wearing, social distancing, and indoor infection control. The results highlighted the significant impact of these measures on individual infection risks associated with indoor activities. Our research contributes to the development of tailored prevention and control strategies, which play a critical role in mitigating the spread of respiratory infections. Thus, this study holds significant implications for technology-facilitated public health services.

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.