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This paper describes a filleting machine designed for the popular Asian freshwater species rohu (Labeo rohita). The machine comprises three key units: conveying, filleting, and water spraying and collection. The filleting unit has two parallel disc blades for precise cutting, with adjustable spacing to accommodate a variety of fish backbone thicknesses. The conveying unit employs belt drives and spring-adjusted entry for smooth transport, while the water management unit sprays and cleans the filleting blades, ensuring efficient fish movement. The machine performed efficiently when set to its optimal settings and showed significant improvements compared to manual filleting while maintaining product quality. [ABSTRACT FROM AUTHOR]

In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches. [ABSTRACT FROM AUTHOR]

Viscoelastic properties of 3D printable peanut‐based food ink were investigated using frequency sweep and relaxation test. The incorporation of xanthan gum (XG) improved the shear thinning behavior (n value ranging from 0.139 to 0.261) and lowered the η*, G′, and G′′ values, thus making food ink 3D printable. The addition of XG also caused a downward shift in the relaxation curve. This study evaluates the possibility of an artificial neural network (ANN) approach as a substitute for the Maxwell three‐element and Peleg model for predicting the viscoelastic behavior of food ink. The results revealed that all three models accurately predicted the decay forces. The inclusion of XG decreased the hardness and enhanced the cohesiveness, so enabling the 3D printing of food ink. The hardness was highly positively correlated with Maxwell model parameters Fe, F1, F2, F3, and Peleg constant k2 (0.57) and negatively correlated with k1 (−0.76). [ABSTRACT FROM AUTHOR]

The fuel consumption model serves as a valuable tool for estimating real-time fuel consumption levels. An accurate fuel consumption model is crucial in providing precise information regarding fuel utilisation and motivating experts to assess the vehicle's economy. The goal of this study was to develop a novel approach by eliminating the measurement of real-time tractor PTO (power take-off) power for fuel consumption prediction and to develop a cloud-infused, server-less, machine learning (ML) based real-time generalised tractor fuel consumption prediction model for any tractor between the power range of 8-48 hp. The fuel consumption prediction models from 18 Machine Learning algorithms were developed, and the extensive data analysis with hyperparameter tuning concluded that the Gradient Boosting Regressor Machine Learning model outperformed the other Machine Learning models with reasonable accuracy (R² = 0.999 for training and 0.914 for testing). Cloud-based serverless Web App and Android App integrated with the Gradient Boosting Regressor based fuel consumption prediction model were developed for the real-time fuel consumption prediction and monitoring of a tractor during field operations. The developed Machine Learning model predicted fuel consumption with a Mean Absolute Percentage Error of 11.43% during real-time field experiments with Mouldboard plough operation. The field validation showed the generalisation ability and efficacy of the developed model, and it can be implemented as a user advisory system for real-time energy-efficient agricultural operations. [ABSTRACT FROM AUTHOR]

Hand-pushed cone penetrometer has been utilized to assess soil compaction based on soil penetration resistance (SPR). However, the manual operation of these devices can be quite challenging and erroneous. The accuracy of the data gathered using such devices can be hindered by the operator's limitation in maintaining a constant-rate of penetration into the soil, thus preventing the attainment of a steady, predefined penetration rate. To address this issue, a constant-rate, IoT-enabled cone penetrometer is designed and constructed. The developed penetrometer measures the SPR across the soil profile with a designed lead screw arrangement that ensures a constant-rate penetration. The device comprises a mechanical system and an electronic control unit that utilizes IoT framework. The mechanical system includes a primary frame, lead screw arrangement and a standard penetrometer probe. The SPR, and depth along with coordinates of location are measured and stored on a web server. A user-friendly customizable web interface and mobile application enable real-time access. The developed penetrometer measures the SPR up to 2000 kPa up to 400 mm depth. Comparative analysis shows that the constant-rate penetrometer (CRP) provides more reliable and less variable resistance measurements compared to the hand-pushed penetrometer (HPP), with significant differences observed at multiple depths. This penetrometer maintains a constant-rate of penetration, ensuring adherence to predefined standards for accurate measurements. [ABSTRACT FROM AUTHOR]

Material properties of composites are identified using a novel hybrid RSM-PSO method in this paper. Different response surface methodology (RSM) methods and particle swarm optimization (PSO) methods are studied initially on a 4 degrees-of-freedom (4DOF) dynamic system on their performance in terms of speed and accuracy. The best combination is used as a hybrid RSM-PSO method to evaluate the performance on system identification of an orthotropic plate along with a 4DOF dynamic system and an isotropic plate. The novelty of the present paper is to identify the composite plate material properties using RSM methods based on time domain signals, which is not hitherto reported in the literature. Also, whereas previous papers have used full factorial design for system identification, here CCDI is used. The input factors (design variables) are the system parameters which are to be identified and the response (objective function) is error sum-of-square of acceleration response with respect to new test system. The performance of the proposed method is also evaluated with the addition of 5% Gaussian noise to simulate the experimental errors. The system parameters of the orthotropic plate were identified with 0% and 0.25% average prediction error with zero and 5% addition of noise respectively by the proposed hybrid RSM-PSO method. It is also showed a much better performance and robustness to noise addition when compared to the other RSM methods in the literature. [ABSTRACT FROM AUTHOR]