Daily Archives: August 22, 2026

Authors: Hanamantray R. Horaginamani, Prakash R. Khedad, Shreyas Suresh Rathod, Varun S. Naik, Prof. Gopinath Rathod

Abstract: Agricultural pesticide spraying is an essential crop-protection operation, but conventional hand-operated, engine-driven and battery-dependent sprayers can impose operator effort, fuel dependence, inconsistent application and chemical loss. The 21 studies reviewed in this paper collectively cover photovoltaic sprayers, multi-nozzle systems, remote and robotic platforms, autonomous aerial spraying, air-assisted atomization, spray-drift management, variable-rate application and machine-vision control. The literature indicates a progression from replacing manual or fossil-fuel power with photovoltaic electric pumping toward systems that also adapt spray delivery to crop geometry and target conditions. Solar-powered trolley and knapsack systems demonstrate the feasibility of photovoltaic operation and reductions in operator burden; remote and robotic systems provide greater separation between the operator and spray plume; and precision systems demonstrate substantial reductions in applied chemical when spray rate is matched to the target. At the same time, solar intermittency, battery capacity, machine mass, nozzle calibration, pressure control, spray uniformity, drift and sensing cost remain important limitations. This review synthesizes the 21 studies through a transparent corpus-based review methodology and identifies design principles relevant to a practical solar-powered multifaceted sprayer. Based on the synthesis, a modular machine architecture is proposed in which solar PV, battery storage, an efficient DC pump, pressure regulation, a filtered multi-nozzle boom and a stable wheeled chassis form the basic platform, while remote control, flow/pressure sensing and machine vision can be added progressively. The review concludes that the most practical development pathway is a layered system that combines renewable energy, mechanical simplicity, adjustable spraying and provision for future precision automation.

DOI: http://doi.org/10.5281/zenodo.22055698

Published by: vikaspatanker

Authors: S S Davanageri, M MGanganallimath, Shreedhar B Biradar, Ajaykumar G Honnalli, Sanjay S Balulad, Shridhar S Palled

Abstract: Agricultural cultivators are important soil-tillage implements used for breaking soil clods, loosening soil and preparing suitable soil conditions for sowing. The performance and reliability of cultivator components are influenced by soil condition, operating parameters, tool geometry, material properties and the loads acting during field operation. This review paper examines published research related to the design, structural behaviour and optimization of agricultural cultivators and their working components, withemphasis on computer-aided design and finite element analysis.The reviewed studies demonstrate the application of numerical methods for evaluating stress, deformation, structural strength and soil–tool interaction. Research on tine cultivators has investigated different shovel geometries and soil conditions, while other studies have examined cultivator shares under different static loading conditions using the finite element methods. Recent work on a combined cultivator working tool has further demonstrated the influence of tine geometry and angle of attack on stress distribution and structural performance. Based on the reviewed literature, computer-aided modelling and finite element analysis provide useful approaches for identifying critical regions and comparing alternative cultivator designs before physical fabrication. It provides a technical foundation for the proposed future work on design optimization and structural analysis of an agricultural cultivator using SolidWorks.

DOI: http://doi.org/10.5281/zenodo.22055188

Published by: vikaspatanker

Authors: Abdulazeez Danjuma, M. S. Aliyu, Zaharradeen S. Iro, Usman Abdullahi Musa, Abdulrrazaq A Umar, Abdullahi Ahmed Talba

Abstract: Pneumonia is a prominent preventable cause of death in children, leading to 14% of all fatalities under five and over 700,000 paediatric deaths annually. Chest radiography is the principal diagnostic tool, but interpretation depends on radiologist availability and inter-observer variability, causing severe bottlenecks in low- and middle-income countries.A hybrid CNN–Transformer architecture with convolutional local feature extraction and multi-head self-attention for binary pneumonia classification on chest radiographs was designed, implemented, and evaluated. Studies used a publicly available chest radiograph dataset of 5,856 pictures (4,273 pneumonia, 1,583 normal). Five convolutional blocks (32→64→64→128→256 filters) with batch normalisation and dropout (0.3–0.5) generate a 6,400-dimensional feature vector, which is reshaped into a token sequence using positional encoding and passed through two Transformer encoder layers (8 attention heads, feed-forward dimension 512 The hybrid model had 92.0% accuracy, 96.1% precision, 92.8% recall, 94.4% F1-score, and 0.998 AUC. Confusion-matrix analysis on the held-out test partition (n = 879; 641 pneumonia, 238 normal) gave 595 true positives, 214 true negatives, 24 false positives, and 46 false negatives Self-attention and convolutional feature extraction increase discriminative performance over CNN-only baselines, with precision outperforming accuracy. External multi-institutional validation, multi-class subtyping, and attention-based interpretability are needed before clinical application.

DOI: https://doi.org/10.5281/zenodo.22053763

Published by: vikaspatanker