Search Results (12)

An advanced artificial intelligence (AI)-powered mobile automated impurity removal system was developed and integrated into potato harvesting machinery for decentralized agricultural environments in Japan. As opposed existing stationary AI systems in centralized processing facilities, this mobile prototype enables on-field impurity removal in real time through a systematic dual-evaluation methodology. The system integrates the YOLOX-small architecture with precision pneumatic actuators and achieves 40–50 FPS processing under dynamic field conditions. Algorithm validation across 10 morphologically diverse potato varieties (Danshaku, Harrow Moon, Hokkaikogane, Kitaakari, Kitahime, May Queen, Sayaka, Snowden, Snow March, and Toyoshiro) using count-based analysis showed exceptional recognition, with potato misclassification rates of 0.08 ± 0.03% (range: 0.01–0.32%) and impurity detection rates of 89.99 ± 1.25% (range: 80.00–93.30%). Cross-farm validation across seven commercial farms in Hokkaido confirmed robust algorithm consistency (PMR: 0.08 ± 0.03%, IDR: 90.56 ± 0.82%) without farm-specific calibration, establishing variety-independent and environment-independent operation. Field validation using weight-based analysis during actual harvesting at 1–4 km/h confirmed successful AI-to-field translation, with 0.22–0.42% potato misclassification and adaptive impurity removal of 71.43–85.29%. The system adapted intelligently, employing conservative sorting under high-impurity loads (71.43% removal, 0.33% misclassification) to prioritize potato preservation while maximizing efficiency under standard conditions (85.29% removal, 0.30% misclassification). The dual-evaluation framework successfully bridged the gap between AI accuracy in laboratory settings and effectiveness in agricultural operations. The proposed AI algorithm surpassed project targets for all tested conditions (>60% impurity removal, <1% potato misclassification). This successful integration demonstrates technical feasibility and commercial viability for widespread agricultural automation, with a validated 50% reduction in labor (four workers to two workers). This implementation provides a comprehensive validation methodology for next-generation autonomous harvesting systems. Full article

In this paper, we explore using the Harrow–Hassidim–Lloyd (HHL) algorithm to address scientific and engineering problems through quantum computing, utilizing the NWQSim simulation package on a high-performance computing platform. Focusing on domains such as power-grid management and climate projection, we demonstrate the correlations of the accuracy of quantum phase estimation, along with various properties of coefficient matrices, on the final solution and quantum resource cost in iterative and non-iterative numerical methods such as the Newton–Raphson method and finite difference method, as well as their impacts on quantum error correction costs using the Microsoft Azure Quantum resource estimator. We summarize the exponential resource cost from quantum phase estimation before and after quantum error correction and illustrate a potential way to reduce the demands on physical qubits. This work lays down a preliminary step for future investigations, urging a closer examination of quantum algorithms’ scalability and efficiency in domain applications. Full article

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial. Full article

To investigate the interaction mechanism between agricultural tillage machinery and soil, this study established a precise simulation model by integrating physical and numerical experiments using typical yellow cinnamon soil collected from western Henan Province, China. The discrete element parameters for soils with varying moisture contents were calibrated based on the Hertz–Mindlin (no slip) contact model. Through Plackett–Burman screening, steepest ascent optimization, and Box–Behnken response surface methodology, a predictive model correlating moisture content, parameters, and repose angle was developed, yielding the optimal contact parameter combination: interparticle static friction coefficient (0.6), soil–65Mn static friction coefficient (0.69), and interparticle rolling friction coefficient (0.358). For the Bonding model, orthogonal experiments coupled with NSGA-II multi-objective optimization determined the optimal cohesive parameters targeting maximum load (673.845 N) and displacement (9.765 mm): normal stiffness per unit area (8.8 × 10⁷ N/m³), tangential stiffness per unit area (6.85 × 10⁷ N/m³), critical normal stress (6 × 10⁴ Pa), critical tangential stress (3.15 × 10⁴ Pa), and bonding radius (5.2 mm). Field validation using rotary tillers and power harrows demonstrated less than 6% deviation in soil fragmentation rates between simulations and actual operations, confirming parameter reliability and providing theoretical foundations for constructing soil-tillage machinery interaction models. Full article

This study presents an experimental and computational analysis of the specific draft (SD) and specific torque (ST) requirements of an energy-efficient tillage implement, the active–passive disk harrow (APDH). Soil bin trials were conducted to develop multiple regression models predicting SD and ST based on operational parameters such as gang angle (α), speed ratio (u/v), soil cone index, and working depth. Model’s accuracy was assessed through statistical indices such as R², RMSE, MIE, and MAE. The high R² and low RMSE confirmed the reliability of the developed models in capturing the relationships between input and output variables. A genetic algorithm-based multi-objective optimization was implemented in MATLAB R2016a to determine optimal operational settings that minimize total power consumption while maximizing soil pulverization. The optimized values of α and u/v were determined to be in the ranges of 35.91° to 36.98° and 3.27 to 3.87, respectively. Model validation with laboratory and field data demonstrated acceptable prediction accuracy despite minor deviations attributed to soil variability and measurement errors. The developed models provide a predictive framework for optimizing tillage performance, aiding in tractor-implement selection, and enhancing energy efficiency in agricultural operations. Full article

In China, there are approximately 36.7 million hectares of available saline–alkali land. The quality of land preparation significantly influences the yield of crops grown in saline–alkali soil. However, saline–alkali soil is highly compacted, and, currently, the market lacks land-preparation products specifically tailored to the unique characteristics of saline–alkali land. The soil crushing performance of existing power harrows fails to meet the requirements for high-quality land preparation, thus affecting crop planting yields. Consequently, it is imperative to conduct research on the design and performance improvement of the soil crushing components of power harrows for saline–alkali land. This paper centers on the key soil crushing component, the harrow blade, and conducts research from the perspectives of kinematics and dynamics. Initially, the ranges of key structural and motion parameters are determined, such as the angle of the harrow blade cutting edge, the thickness of the of the harrow blade cutting edge, and the ratio of the circumferential speed to the forward speed. Subsequently, through simulation tests integrating the Discrete Element Method (DEM) and the Box–Behnken Design (BBD), the optimal parameter combination is identified. The impact of the forward speed and the rotational speed of the vertical-shaft rotor on soil disturbance is analyzed. The relationship between soil disturbance and soil heaping is explored, and an optimal forward speed of around 6 km/h is determined. Field tests are conducted to verify the cause of soil heaping. The test results show that the soil crushing rates are all above 85%, with an average soil crushing rate of 88.66%. These test results have achieved the predetermined objectives and meet the design requirements. Full article

Tractors operate under varying and unpredictable conditions, making energy management strategies insufficient for maintaining system power dynamics, which often leads to reduced traction power and overall efficiency. To overcome this challenge, a fuzzy-following energy management strategy was developed. This approach utilizes fuzzy control based on energy following to optimize the tractor’s energy output, ensuring more stable power delivery. A target tractor model was constructed using CRUISE, and joint simulations were carried out via the CRUISE-Simulink interface. The results demonstrated that the fuzzy-following strategy stabilized the battery’s state of charge (SoC) and improved fuel economy. The strategy was implemented for controlling a hybrid tractor, and its effectiveness and stability were validated through drivetrain system tests and real vehicle trials under light load, plowing, and power harrowing conditions, successfully achieving power balance under these diverse operating scenarios. Comparative tests between the hybrid tractor using the fuzzy-following strategy and a powershift tractor revealed that the hybrid tractor exhibited superior plowing efficiency and fuel economy under plowing and power-harrowing conditions. Full article

The rotary tillage knife roller, as one of the typical soil-touching parts of the tillage equipment cutting process, is in direct contact with the soil. During the cutting process, there are problems related to structural bending, deformation, and high power consumption, caused by impact and load, and it is difficult to observe the micro-change law of the rotary tillage tool and soil. In view of the above problems, we took the soil of the cotton experimental field in Shihezi, Xinjiang, and the soil-contacting parts of the rotary tillage equipment, specifically the rotary tiller roller, as the research subject. Using the finite-element method (FEM) to simulate the structure of the rotary tiller with different bending angle parameters, we obtained its average stress and deformation position information, and obtained a range linear relationship between the bending angle and the structural performance of the rotary tiller tool. Using discrete element method (DEM)-based simulation to build the corresponding contact model, soil particle model, and soil–rotary tillage knife roll interaction model to simulate the dynamic process of a rotary tillage knife roll cutting soil, we obtained the change rules of the soil deformation area, cutting process energy, cutting resistance, and soil particle movement. By using the orthogonal simulation test and the response surface method, we optimized the kinematic parameters of the rotary tiller roller and the key design parameters of a single rotary tiller. Taking the reduction of cutting power consumption as the optimization goal and considering the influence of the bending angle on its structural performance, the optimal parameter combination was obtained as follows: the forward speed was 900 m/h, the rotation speed was 100 rad/min, the bending angle was 115°, and the minimum power consumption of the cutter roller was 0.181 kW. The corresponding average stress and deformation were 0.983 mm and 41.826 MPa, which were 15.8%, 13%, and 7.9% lower than the simulation results of power consumption, stress, and deformation under the initial parameter setting, respectively. Finally, the effectiveness of the simulation optimization model in reducing power consumption and the accuracy of the soil-cutting simulation were verified by a rotary tilling inter-field test, which provided theoretical reference and technical support for the design and optimization of other typical soil-touching parts of tillage and related equipment, such as disc harrow, ploughshare, and sub-soiling shovel. Full article

Conventional seedbed preparation is based on deep ploughing followed by lighter and finer secondary tillage of the superficial layer, normally performed by machines powered by the tractor’s Power Take-Off (PTO), which prepares the seedbed in a single pass. Conservation methods are based on a wide range of interventions, such as minimum or no-tillage, by means of machines with passive action working tools which require two or more passes The aim of this study was to assess both the power-energy requirements of conventional (power harrows and rotary tillers with different working width) and conservation implements (disks harrow and combined cultivator) and the soil tillage quality parameters, with reference to the capability of preparing an optimal seedbed for wheat planting. Field tests were carried out on flat, silty-clay soil, using instrumented tractors. The test results showed significant differences among the operative performances of the two typologies of machines powered by the tractor’s PTO: the fuel consumption, the power and the energy requirements of the rotary tillers are strongly higher than power harrows. However, the results also showed a decrease of these parameters proceeding from conventional to more conservation tillage implements. The better quality of seedbed was provided by the rotary tillers. Full article

Farm machinery selection, operation and management directly impact crop cultivation processes and outputs. A priori quantification of technical and financial needs allows definition of proportionate distribution and management of available resources and simplification of selection process. Appropriate planning, association and adjustment of the power unit and implement are required for soil cultivation. Consideration of functional parameters of the implement, their proper estimation and operation directly impact the soil structure, productivity and return on investment. Thus, a modelling approach was implemented for the definition of possible parameter-price relations for tillage equipment. The performed analysis allowed us to investigate the main relevant parameters, quantify their impact, and elaborate forecasting models for price, power, mass and working width. The significant relevance of the technical parameters and adjustment issues were outlined for each tillage implement group. For harrows and cultivators, the dependencies between studied parameters expressed better predictive qualities, especially for price-mass relation (R² > 0.8). While for ploughs power and mass relation had a primary output (R² = 0.7). The prediction features of the models provided reliable results for the estimation of the indicative values of the price and parameters of the implements. Full article

Soil seed banks were sampled in undisturbed soil and after soil had been disturbed by tillage (tine, harrow or plough). Seeds were sorted by size and shape, and counted. Size-number distributions were fitted by power law equations that allowed the identification of self-similarity and self-affinity. Self-affinity and thus non-random size-number distribution prevailed in undisturbed soil. Self-similarity and thus randomness of size-number distribution prevailed after tillage regardless of the intensity of disturbance imposed by cultivation. The values of fractal dimensions before and after tillage were low, suggesting that short-term, short-range factors govern size-number distribution of soil seed banks. Full article