Other affiliations: Kyungpook National University
Bio: Eiji Inoue is an academic researcher from Kyushu University. The author has contributed to research in topics: Tractor & Combine harvester. The author has an hindex of 12, co-authored 137 publications receiving 541 citations. Previous affiliations of Eiji Inoue include Kyungpook National University.
Papers published on a yearly basis
TL;DR: In this article, a scale model tractor was developed allowing changes to these factors, and the tractor lateral stability was evaluated in terms of the roll angle, lateral-load transfer ratio, and Phase I overturn index.
Abstract: Statistics show that lateral overturns are the most frequent fatal accidents involving tractors. There is thus much research interest in improving tractor lateral stability. Previous research has discovered the effects of various factors on tractor dynamic responses. While these factors have been analysed separately, their relative significance with respect to other factors remain uncertain. Furthermore, the practical limits of what operators can do have not been considered. The present study assumed a possible case that a tractor operator has several spare tyres of different types and service condition. Additionally, the ballast weight, track width, and implement position can usually be controlled before operation. A scale model tractor was thus developed allowing changes to these factors. The model tractor was designated to pass over typical farming road surfaces. Moreover, the tractor lateral stability was evaluated in terms of the roll angle, lateral-load transfer ratio, and Phase I overturn index. Employing the Taguchi method, we arranged experiments and assessed the applicability of the three kinds of indexes regarding tractor Phase I overturn. Results revealed that the roll angle did not well reflect the initiations of overturns. Compared with the lateral-load transfer ratio, the Phase I overturn index had more convincing factorial effects on tractor stability. Further investigation of the suggested tractor configuration supported this conclusion by comparing predicted and experimental results. In practical cases, this approach may provide a reference for engineers to help operators improve driving safety with limited spare parts.
TL;DR: The developed framework could help to maintain the sustainability of environmental monitoring under unstable network connection over 80% availability of the data with local offline measurement up to 24% of the total entries and increase the system flexibility in the adjustment of the system configuration remotely.
Abstract: Display Omitted Remote monitoring and control framework under unstable network connection.The framework consists of local and global management subsystem.Evaluated on tropical horticulture monitoring and soil moisture content control. This study focuses on the development and evaluation of a remote field environmental monitoring and control framework, implementing a local-global management strategy to overcome the unstable network connection in the rural area. The framework consists of environmental monitoring and control node as the local management subsystem (LMS), and the web data providing and system management as the global management subsystem (GMS) to establish a simple and flexible remote environmental monitoring and control based on a cloud platform. The supporting features are online and offline environmental monitoring, synchronization of system configuration, actuation, and offline management. Two field tests were conducted to verify its performances and functionalities, (1) environmental monitoring on tropical horticulture cultivation in Yogyakarta, Indonesia, and (2) implementation of the monitoring and control for automatic drip irrigation control based on soil moisture content for tomato. As the result of the first test, the developed framework could help to maintain the sustainability of environmental monitoring under unstable network connection over 80% availability of the data with local offline measurement up to 24% of the total entries. From the second test result, the framework could support the real-time monitoring and control of soil moisture content as well as increase the system flexibility in the adjustment of the system configuration remotely. The control system has 0.78% error (E) and 99.2% in-range soil moisture content (L
Abstract: In the present study, the relationships between the deflection and deflection force acting on a bunch of crop stalks in the gathering operations of a combine harvester reel were analysed. Two mechanical crop models based on bending theory with regard to an elastic beam were applied in this analysis. In the first model, the sectional heterogeneity of a stalk is expressed as the different flexural rigidity of each internode. In the second model, the effect of a crop ear is added to the first model. The application of the mechanical models to the reel operations and the analytical methods of the deflection force were investigated using experiments involving reel operations. The results showed that the effect of both the buckling load due to the weight of the ear and the vertical force component acting on the crop stalks increased noticeably with the increment of deflection imposed by the gathering operations. The prediction of the relationships between deflection and deflection force in the second model, in which the effect of the ear weight was considered, involved a certain amount of error due to the effect of the initial posture of the crop stalk and the vertical force component, but coincided approximately with the experimental results. Thus, it was suggested that the model that took into account the effect of the crop ear was useful for investigating reel–crop interactions, and that the analytical accuracy of the deflection force would be increased by considering the effect of the vertical force component. Further, based on the above results, a posture analysis of a bunch of crop stalks during reel operations was conducted.
TL;DR: In this article, the authors developed stability indicators based on a more general model for dynamic Phase I tractor overturns and side-slip, and determined critical tractor speeds for various ground conditions by considering the zero values of the tractor stability indicators.
Abstract: Tractor overturns are serious potential hazards for operators. While rollover protective structures (ROPS) protect operators passively, greater protection can be achieved through theoretical prediction of a potential overturn. Given effective warning, an operator can act to correct a tractor's motion when a tyre is about to lose contact with the ground. Such a loss of contact is associated with the initiation of a Phase I tractor overturn. However, it remains unclear how the initiation of tractor overturn is influenced by certain factors. Furthermore, the current mathematical models for tractors should be further extended for general utilisation. This study was conducted to develop stability indicators based on a more general model for dynamic Phase I tractor overturn. We considered practical tractor configurations and motion characteristics in a three-dimensional (3D) reference frame in formulating the mathematical model. Tractor stability indicators for overturn and sideslip were derived from force calculations. A parametric study was conducted using an example tractor. The tractor speed and slope angle were found to affect the overturning stability significantly. The coefficient of maximum static friction was found to be the main factor contributing to tractor sideslip. Critical tractor speeds for various ground conditions were identified by considering the zero values of the tractor stability indicators. The critical tractor speed was determined as a function of the maximum static friction and the slope angle. By providing a display device based on ergonomics principles, the results of this study can be further implemented in the form of guidance to operators.
TL;DR: In this paper, the authors developed predictive models for yield and protein content of brown rice that can provide useful knowledge to support farmer's management decision-making, utilizing data sets from 47 paddy fields where rice was produced under various environments and management styles.
Abstract: We developed predictive models for yield and protein content of brown rice.The models were built based on pattern recognition using SVMs.Explanatory variables included growth, nutrition and meteorological conditions.The typical accuracy was within 1tha-1 in yield and 0.8% in protein content.The model reasonably visualized patterns for yield and protein content classes. Rice production in Japan is facing problems of yield and quality instability owing to recent climate changes, aging of farmers, and a decrease in the farmer population. Thus, it is becoming important to develop an improved rice production technology that utilizes collected data about rice production rather than relying on the conventional technology that is based on the experience and knowledge of individual farmers. We developed predictive models for yield and protein content of brown rice that can provide useful knowledge to support farmer's management decision-making, utilizing data sets from 47 paddy fields where rice was produced under various environments and management styles. Support vector machines (SVMs) were applied to build the predictive models based on explanatory variables representing the growth and nutrition conditions after the heading stage and the meteorological environment after the late spikelet initiation stage. The models achieved quantitative accuracy that was within approximately 1tha-1 in yield for 85.1% of the total data sets and within 0.8% in protein content for 76.6% of the total data sets, respectively. Further, patterns of explanatory variables classified in three classes of yield and protein content, which were visualized by the predictive models, were reasonable in terms of knowledge of crop science. We found that the predictive models using SVMs had the potential to describe a relation between yield or protein content and multiple explanatory variables that reflected diverse rice production in actual fields, and could provide useful knowledge for decision-making of topdressing and basal fertilization.
TL;DR: In this paper, the authors highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation, which is necessary for a successful transition to urban agriculture.
Abstract: Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture (CEA) facilities that projected the image of plant factories for urban agriculture The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings Successful deployment of CEA for urban agriculture requires many components and subsystems, as well as the understanding of the external influencing factors that should be systematically considered and integrated This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation, which is necessary for a successful transition to urban agriculture This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials, environment perception and data sharing, and advanced microclimate control and energy optimization models This research highlighted urban agriculture and its derivatives, including vertical farming, rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population, environmental degradation, and urbanization that are threatening food security Finally, several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture Keywords: smart agriculture, greenhouse modelling, urban agriculture, vertical farming, automation, internet of things (IoT), wireless sensor network, plant factories DOI: 1025165/jijabe201811013210 Citation: Shamshiri R R, Kalantari F, Ting K C, Thorp K R, Hameed I A, Weltzien C, et al Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture Int J Agric & Biol Eng, 2018; 11(1): 1–22
TL;DR: This review is addressing an analytical survey of the current and potential application of Internet of Things in arable farming, where spatial data, highly varying environments, task diversity and mobile devices pose unique challenges to be overcome compared to other agricultural systems.
Abstract: The Internet of Things is allowing agriculture, here specifically arable farming, to become data-driven, leading to more timely and cost-effective production and management of farms, and at the same time reducing their environmental impact. This review is addressing an analytical survey of the current and potential application of Internet of Things in arable farming, where spatial data, highly varying environments, task diversity and mobile devices pose unique challenges to be overcome compared to other agricultural systems. The review contributes an overview of the state of the art of technologies deployed. It provides an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Lastly, it presents some future directions for the Internet of Things in arable farming. Current issues such as smart phones, intelligent management of Wireless Sensor Networks, middleware platforms, integrated Farm Management Information Systems across the supply chain, or autonomous vehicles and robotics stand out because of their potential to lead arable farming to smart arable farming. During the implementation, different challenges are encountered, and here interoperability is a key major hurdle throughout all the layers in the architecture of an Internet of Things system, which can be addressed by shared standards and protocols. Challenges such as affordability, device power consumption, network latency, Big Data analysis, data privacy and security, among others, have been identified by the articles reviewed and are discussed in detail. Different solutions to all identified challenges are presented addressing technologies such as machine learning, middleware platforms, or intelligent data management.
TL;DR: This paper presents the CPS taxonomy via providing a broad overview of data collection, storage, access, processing, and analysis, and discusses big data meeting green challenges in the contexts of CPS.
Abstract: The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors, such as environmental monitoring, mobile-health systems, intelligent transportation systems, and so on. The information and communication technology sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets, and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing, and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS requires cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that are continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access, and analytics. We also discuss big data meeting green challenges in the contexts of CPS.
TL;DR: In this paper, the bending forces were measured by a cantilever test in the field at different moisture contents and the bending stress and the modulus of elasticity were calculated from these data.
Abstract: Bending stress, modulus of elasticity, shearing stress and specific shearing energy were determined for sunflower (Heliantus annus L.) stalk. The bending forces were measured by a cantilever test in the field at different moisture contents and the bending stress and the modulus of elasticity were calculated from these data. For measuring the shearing forces, the stalk specimens were severed by using a computer aided cutting apparatus. The specific shearing energy was calculated by using the area under the shearing force versus displacement curve. The bending tests were conducted at four different moisture contents. The bending stress decreased as the moisture content increased. The average bending stress value varied between 37.77 and 62.09 MPa. The modulus of elasticity in bending also decreased as the moisture content and diameter of stalks increased. The average modulus of elasticity varied between 1251.28 and 2210.89 MPa. The shearing stress and the specific shearing energy were also determined at five moisture contents according to the stalk regions. The results showed that the shearing stress and the specific shearing energy increased as the moisture content increased. The maximum shearing stress and specific shearing energy were 1.07 MPa and 10.08 mJ mm(-2), respectively. Both the shearing stress and the specific shearing energy were found to be higher in the lower region of the stalk due to structural heterogeneity.
TL;DR: The aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management and provide recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain.
Abstract: It is essential to increase the productivity of agricultural and farming processes to improve yields and cost-effectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest sub-vertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.