Industrial Production Research Articles

Preparation of Single-Crystal Li-Rich Mn-Based Layered Oxides with Excellent Electrochemical Performance via Simple Stepwise High-Temperature Sintering.

Li-rich Mn-based layered oxides have attracted extensive attention in lithium-ion battery cathode materials due to their high specific capacity, wide operating voltage, and low cost. However, the large-scale production of single-crystal Li-rich Mn-based layered oxides remains a significant challenge. Herein, the morphology, structure, and electrochemical properties of a series of samples (Li1.2Ni0.13Co0.13Mn0.54O2) prepared by the solid-state method and the molten-salt flux method were assessed. Single-crystal particles exactly could be prepared by potassium chloride (KCl), but its electrochemical performance was inferior and the molten-salt flux method was too complicated for industry. Surprisingly, we found that water washing and annealing processes could enhance the cycling performance. Furthermore, single-crystal Li1.2Ni0.13Co0.13Mn0.54O2 with a particle size of 536 nm was successfully prepared by simple stepwise sintering (950 °C for 7 h, 1000 °C for 1 h, and 850 °C for 2 h), which delivered a specific capacity of 274.9 mAh·g-1 between 2.0 and 4.8 V at 0.1 C with 77.4% initial Coulombic efficiency and 82.1% capacity retention after 100 cycles at 0.1 C. This study may provide theoretical guidance for the industrial production of single-crystal Li-rich Mn-based layered oxides.

Ultrathin Self-Assembled Monolayer for Effective Silicon Solar Cell Passivation.

Passivation technology is crucial for reducing interface defects and impacting the performance of crystalline silicon (c-Si) solar cells. Concurrently, maintaining a thin passivation layer is essential for ensuring efficient carrier transport. With an ultrathin passivated contact structure, both Silicon Heterojunction (SHJ) cells and Tunnel Oxide Passivated Contact (TOPCon) solar cells achieve an efficiency surpassing 26%. To reduce production costs and simplify solar cell manufacturing processes, the rapid development of organic material passivation technology has emerged. However, its widespread industrial production is hindered by environmental safety concerns, such as strong acid corrosion and biological and ecological safety issues. Here, we discovered a low-cost self-assembled monolayer (SAM) hole-selective transport material known as 2PACz ([2-(9H-carbazol-9-yl) ethyl] phosphonic acid) with phosphate groups to form c-Si solar cells for the first time. The ultrathin film of 2PACz with phosphate groups can establish strong and stable P-O-Si bonds on the silicon surface. Meanwhile, like 2PACz, a uniform ultrathin film with a carbazole function group can offer electron-localizing and thus hole-selective properties, which provides ideas for studying dopant-free silicon solar cells. As a result of such interfacial passivation engineering, it plays an important role in repairing porous structures, such as pyramid-textured silicon surfaces, and cutting losses during the commercialization of c-Si solar cells. Crucially, this advancement offers insights for the development of new high-efficiency ultrathin film passivation methods in the postsilicon era.

Organic Food Demand Boosts Organic Food Production? Closer Look Into a Bibliometric Analysis of Organic Food Manufacturing Research from the Period 1998 to 2023

Introduction: Given the significance of organic food research in food manufacturing industry, bibliometric analysis within the period of 1998 to 2023 was utilised to provide a review of the body of work published in this area. The framework and central idea of the literature on organic food production in food and beverages industry by using the bibliometric analysis of the literature was further determined. To comprehend the thematic organisation and knowledge of organic food research in food manufacturing industry, the bibliographic coupling and co-citation analysis are carried out. Additionally, research trends and research direction in organic food development in food and beverages industry were fully understand by utilised co-occurrence and confluence analysis. The results of this study has suggested that there are more research are related to organic food demand and consumption which are linked to production of the organic food. Objective: Given the significance of organic food research in food manufacturing industry, bibliometric analysis within the period of 1998 to 2023 was utilised to provide a review of the body of work published in this area. Theoretical Framework: Organic production and terms related to organic food were combined in this study's nomenclature. The keywords linked with each sector had to be used to make sure that this research addressed all aspects of organic food and organic production. Method: The Scopus database, which is the largest biographical database, was used for data extraction and analysis. The research evolution and themes of organic food research in the food manufacturing industry were covered in the study for various time periods. Results and Discussion: The research evolution and themes of organic food research in the food manufacturing industry were covered in the study for various time periods. This study outlined the evolution of organic food research in the food manufacturing industry as well as potential research topics for academics.To further comprehend the research paradigm from high-quality publications, future studies may use bibliometric analysis on SCI, SSCI, and ABDC-listed articles solely. Research Implications: The current study has a number of ramifications for academics, businesspeople, marketers, and scholars. They ought to learn about the general studies that have been done in this field. They can use these papers to address the current issues in academia and business by being aware of the important contributors to this field of research and their motivations for doing so. Originality/Value: By examining many facets of organic food research, researchers have made significant contributions to this field. Studies are primarily focused on consumers’ demand facet, but there are fewer studies on the supply side from food and beverages industry. The first study on organic food development was carried out in 1998, according to the Scopus database. Since its inception till the present, organic food has taken many different forms. As there is no study that specifically focuses on organic food production in food and beverages industry, there is a need to research develop in the field of development of organic food production in food and beverages industry.

Applications of Machine Learning in Real-Time Control Systems: A Review

Abstract Real-time control systems(RTCS) have become an indispensable part of modern industry, finding widespread applications in fields such as robotics, intelligent manufacturing and transportation. However, these systems face significant challenges, including complex nonlinear dynamics, uncertainties and various constraints. These challenges result in weakened disturbance rejection and reduced adaptability, which make it difficult to meet increasingly stringent performance requirements. In fact, RTCS generate a large amount of data, which presents an important opportunity to enhance control effectiveness. Machine learning, with its efficiency in extracting valuable information from big data, holds significant potential for applications in RTCS. Exploring the applications of machine learning in RTCS is of great importance for guiding scientific research and industrial production. This paper first analyzes the challenges currently faced by RTCS, elucidating the motivation for integrating machine learning into these systems. Subsequently, it discusses the applications of machine learning in RTCS from various aspects, including system identification, controller design and optimization, fault diagnosis and tolerance, and perception. The research indicates that data-driven machine learning methods exhibit significant advantages in addressing the multivariable coupling characteristics of complex nonlinear systems, as well as the uncertainties arising from environmental disturbances and faults, thereby effectively enhancing the system's flexibility and robustness. However, compared to traditional methods, the applications of machine learning also faces issues such as poor model interpretability, high computational requirements leading to insufficient real-time performance, and a strong dependency on high-quality data. This paper discusses these challenges and proposes potential future research directions.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Patriarcapitalism? Towards an Intertwined History of Inheritance law and Capitalism from the Middle Ages to the Industrial Revolution (c. 1350–1850)

Abstract This article examines the patriarchal nature of inheritance law in late medieval and early modern England, the existence of an aristocratic offensive in the field of law against women’s inheritance rights between the 14th and the 18th century, and the relation between this offensive and the rise of agrarian capitalism. It then investigates the favorable effects of this legal legacy on the accumulation and concentration of the means of agrarian, coal and industrial commodity production in the hands of a minority of male aristocrats and bourgeois who played a significant role in the making of the Industrial Revolution.

Presence of Soya in Industrial and Homemade Sausage Production in Kosovo and Its Reflection on Fatty Acid Profile

In the present study, we investigated to what extent soybean was used in industrial and homemade sausage produced in Kosovo, as well as its potential impact on the fatty acid profile. In total, 63 samples, 42 industrial and 21 traditional sausages, were collected either from the market or the production site. All samples were tested by means of real-time PCR for the detection and quantification of soy content, as well as the GC-FID to determine the potential reflection in the fatty acid profile. The presence of soy DNA was detected in 54 out of 63 samples. In total, 41 out of 42 industrial sausage samples were positive for soy DNA, with an average ct value of 22.60 and a standard deviation (SD) of 4.28, whereas 13 out of 21 traditional sausage samples were positive for soy DNA, with a mean ct value of 23.36 and a standard deviation (SD) of 4.56. There was a statistically significant difference in means between industrial sausage and traditional sausage, with p ≤ 0.001 based on CT values (ANOVA test). We investigated the correlation between ct values in real-time PCR for soy DNA with each fatty acid content. There is a moderate correlation of soy DNA ct values with C16:0 palmitin (decrease), C18:0 stearic acid (decrease), C18:1 oleic acid (increase), and overall saturated fatty acids (decrease). With the exception of C14:0 Myristin, C18:1 oleic, and C20:0 Arachin acids and monosaturated fats, the ANOVA test reveals a significant difference in means between groups for the majority of the fatty acids between industrial sausages and traditional sausages. The current study demonstrates that the fatty acid composition of sausages is influenced by the amount of soy present in them. The extent to which other components affect the fatty acid profile is unknown. However, an increase in oleic acid and a decrease in stearic and overall saturated fatty acids are expected.

Towards full autonomy in mobile robot navigation and manipulation

AbstractRobot autonomy is a key for automatizing industrial production plants, facilitating responsive search and rescue (SAR), and for interacting in urban settings. This work presents mobile systems capable of carrying out these tasks and summarises experience from practical deployments in three different domains for navigation. For industry, a mobile manipulator is presented, which is used for dynamic precise docking and object handling. For SAR, a semi-autonomous system is presented which is used for sampling hazardous materials, manipulating tools, and for localizing nuclear radiation sources. To improve urban logistics, a mobile robot autonomously navigates complex outdoor environments using semantic mapping techniques. Future work will explore further advancements that strive toward increased flexibility, reasoning and universal applicability of mobile robots. The goal is to reduce training, onboarding and tool changing times to reduce manual interfacing times.

High-performance PANI sensor on silicon nanowire arrays for sub-ppb NH3 detection

For industrial production and disease diagnosis, real-time detection of low concentrations of NH3 is crucial, necessitating a gas-sensitive sensor compatible with integrated processes and exhibiting excellent performance. Herein, we employed wet etching and rapid in-situ polymerization on silicon nanowire substrates to grow polyaniline fibers, thereby fabricating NH3 gas sensors with p-p heterojunction and three-dimensional network structures. Characterization and gas sensing performance testing were conducted. The results demonstrate the outstanding NH3 detection capabilities of the sensor, providing stable responses down to concentrations as low as 1 ppb, which indicates its LOD is one to two orders of magnitude lower than current similar products. It also exhibits verified selectivity and long-term reliability. The excellent sensing performance is attributed to the high surface area from the silicon nanowire structure and efficient synergy of p-p heterojunction. Additionally, the influence of doping types of the substrates and annealing process were explored. This work serves as a reference for the design of silicon-based gas sensors with high sensitivity, low detection limits, and extended operational lifetimes, suitable for deployment in commercial integrated monitoring systems.

Industrial Production Networks and Small Towns: A Case Study from Algeria

This paper investigates the conditions and consequences of integrating small towns into industrial production networks. It is based on empirical research conducted in Algeria, a hydrocarbon-dependent rentier economy characterized by significant regional inequalities and the political aims of economic diversification and spatial rebalancing. Elaborating the case study of a state-owned cement factory in the small town of Sigus, the research provides insights into the multiple roles of the state in shaping production network integration and the characteristics of small towns as economic locations. The methodology combines secondary data and information with primary research based on semi-structured interviews. It reveals the importance of a multi-scalar regional framework in production network integration, whereby national factors played a key role due to the centralized Algerian state, the state-owned character of the investing company, and the shortcomings of the small town’s local environment. It emphasizes the contradictory impacts of production network integration in economic, social, and environmental terms, primarily on a local level. These contradictions underscore the necessity for critical evaluations to maximize the benefits of production network integration while mitigating its adverse effects. They also call for the more consistent involvement of the local community in similar economic development decisions. Notably, this research contributes significantly to the existing body of literature by addressing the underexplored topic of integrating small towns into production networks within the Algerian context. Doing so offers a more nuanced understanding of the particular economic, social, and environmental dynamics at play in these locations, thereby enriching the discourse on economic development strategies for small towns in rentier economies like Algeria.

Calix[4]arene-decorated polymers of intrinsic microporosity for lithium isotopes separation

Due to the importance of lithium isotopes in the nuclear energy industry, there are increasing number of researchs on the lithium isotopes separation in recent years. However, the adsorbents used for lithium isotopes separation suffer from the low adsorption capacity and isotopes separation factor in aqueous phase, and is difficult to meet the requirements for industrial lithium isotopes production. In this work, two types of calix[4]arenes with different rim groups were incorporated into the polymer backbone. Both calix[4]arene decorated PIMs exhibited good lithium adsorption capacities in aqueous phase. Among them, 4-tert-butyl-calix[4]arene decorated PIM showed excellent lithium isotopes separation performance with a maximum lithium adsorption capacity of 67.99 mg·g−1 and an isotopes separation factor of 1.034 ± 0.003 in aqueous phase. The ease of preparing and stable cycling performance makes it potentially useful for industrial lithium isotopes production.

Recent Trends in Laboratory Preparation and Industrial Production Methods on Cocrystals

Recent Trends in Laboratory Preparation and Industrial Production Methods on Cocrystals

Food-fuel nexus beyond mean-variance: New evidence from a quantile approach

This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.

Unsupervised Anomaly Detection via Normal Feature-Enhanced Reverse Teacher–Student Distillation

In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection methods based on reverse distillation (RD) have become a mainstream choice, which has attracted extensive research due to their excellent anomaly detection performance. However, there is a problem of “feature leakage” in the RD model, which may lead to non-anomalous regions being incorrectly identified as defects. To solve this problem, we propose a Normal Feature-Enhanced Reverse teacher–student Distillation (NFERD) method. Specifically, we designed and incorporated a normal feature bank (NFB) module into the basic RD network. This module stores normal features extracted by the teacher model, assisting the student model in learning normal features more efficiently, thereby addressing the problem of “feature leakage”. In addition, to effectively fuse the feature maps extracted by the student model with the feature maps in NFBs, we designed a Hybrid Attention Fusion Module (HAFM), which ensures the preservation of key information during the feature fusion process by the parallel processing of spatial and channel attention mechanisms. Through experimental verification on two publicly available datasets, i.e., MVTec and KSDD, our method outperformed the existing mainstream methods in both image-level and pixel-level anomaly detection. Specifically, we achieved an average I-AUROC score of 99.32% on MVTec and a 98.75% P-AUROC on the KSDD, showing clearer segmentation results, especially in complex scenarios. Furthermore, our method surpassed the second-best method by over 1.4% PRO on MVTec, demonstrating its effectiveness.

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

Cannabis-Based Phytocannabinoids: Overview, Mechanism of Action, Therapeutic Application, Production, and Affecting Environmental Factors

This review provides an overview of cannabis-based phytocannabinoids, focusing on their mechanisms of action, therapeutic applications, and production processes, along with the environmental factors that affect their quality and efficacy. Phytocannabinoids such as THC (∆9-tetrahydrocannabinol), CBD (cannabidiol), CBG (cannabigerol), CBN (cannabinol), and CBC (cannabichromene) exhibit significant therapeutic potential in treating various physical and mental health conditions, including chronic pain, epilepsy, neurodegenerative diseases, skin disorders, and anxiety. The cultivation of cannabis plays a crucial role in determining cannabinoid profiles, with indoor cultivation offering more control and consistency than outdoor methods. Environmental factors such as light, water, temperature, humidity, nutrient management, CO2, and the drying method used are key to optimizing cannabinoid content in inflorescences. This review outlines the need for broader data transfer between the health industry and technological production, especially in terms of what concentration and cannabinoid ratios are effective in treatment. Such data transfer would provide cultivators with information on what environmental parameters should be manipulated to obtain the required final product.

Systematic Review of Quantitative Risk Quantification Methods in Construction Accidents

Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies.

Isolation and identification of epiphytic Pichia kudriavzevii from loquat peels and investigation of its fermentation characteristics for liquor production.

During the process of fruit wine production, yeast plays a crucial role in influencing the taste, flavor, and overall quality of the wine. This study aims to enhance the flavor and quality of loquat wine by isolating strains of Pichia kudriavzevii (P. kudriavzevii) with desirable winemaking characteristics from loquat fruit fermentation broth. A total of 12 strains of P. kudriavzevii were isolated and subjected to morphological and molecular biological identification. Their fermentation performance, ethanol production, ester production, hydrogen sulfide production, killer activity, and tolerance were evaluated. The results revealed that strains Q-2, Q-9, Q-10, Q-12, Q-20, and Q-42 exhibited robust growth and strong tolerance under conditions of 40°C temperature, 12% ethanol concentration, 350g/L glucose concentration, and pH 2.8. Strain Q-42 demonstrated the strongest gas production capacity, killer activity, and good ester and ethanol production. As a highly active fermentation strain with excellent wine making characteristics, P. kudriavzevii Q-42 provides a valuable yeast resource for the industrial production of loquat wine and offers technical support for improving the overall quality of loquat wine.

Adsorption and Sensing Properties of Ni-Modified InSe Monolayer Towards Toxic Gases: A DFT Study

The emission of toxic gases from industrial production has intensified issues related to atmospheric pollution and human health. Consequently, the effective real-time monitoring and removal of these harmful gases have emerged as significant challenges. In this work, the density functional theory (DFT) method was utilized to examine the adsorption behaviors and electronic properties of the Ni-decorated InSe (Ni-InSe) monolayer when interacting with twelve gases (CO, NO, NO2, NH3, SO2, H2S, H2O, CO2, CH4, H2, O2, and N2). A comparative assessment of adsorption strength and sensing properties was performed through analyses of the electronic structure, work function, and recovery time. The results show that Ni doping enhances the electrical conductivity of the InSe monolayer and improves the adsorption capabilities for six toxic gases (CO, NO, NO2, NH3, SO2, and H2S). Furthermore, the adsorption of these gases on the Ni-InSe surface is characterized as chemisorption, as indicated by the analysis of the adsorption energy, density of states, and charge density difference. Additionally, the adsorption of CO, NO, NO2, and SO2 results in significant alterations to the bandgap of Ni-InSe, with changes of 18.65%, 11.37%, 10.62%, and −31.77%, respectively, underscoring its exceptional sensitivity. Moreover, the Ni-InSe monolayer exhibits a moderate recovery time of 3.24 s at 298 K for the SO2. Consequently, the Ni-InSe is regarded as a promising gas sensor for detecting SO2 at room temperature. This research establishes a foundation for the development of an Ni-InSe-based gas sensor for detecting and mitigating harmful gas emissions.

Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-arid

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020).