Journal of Scientific Research and Technology

Application of Deep Learning Technique for Tomato Maturity Stage Prediction

2025-07-01T11:49:45+00:00

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural
applications, such as harvesting, grading, and quality control. Primary objective to develop an automated system
capable of identifying various defects in tomatoes and providing relevant treatment suggestions. Leveraging deep
learning techniques, a CNN model is trained to classify tomatoes into four categories: Damaged, Old, Ripen, and
Unripen. The implementation involves training a convolutional neural network and testing dataset of tomato images,
for classification, and deploying model intended real-time predictions. Project has potential to improve effectiveness of
tomato harvesting & reduce waste. The CNN model then predicts the stage of the tomato, providing the probability of
the prediction. Alongside the classification result, the system offers detailed information on the cause of the defect,
appropriate treatment methods, and nutritional content, aiding users in making informed decisions regarding the
tomatoes usability. The implementation ensures a robust and efficient detection mechanism, even in varying lighting
conditions and backgrounds. An Accuracy of 94.48% was attained when taught sculpt was used to make prediction on
the test dataset.

Multifunctional Agricultural Robot

2025-07-01T12:05:10+00:00

This paper presents the design, development, and implementation of a multipurpose agricultural robot aimed at
automating essential farming tasks to enhance productivity and reduce labor. The robot is capable of digging soil,
levelling mud, and spraying water and fertilizers, operating on a combination of battery and solar energy. Key
components include a relay switch, Bluetooth modules for user interaction, and various sensors to ensure precise
operation. The hands-free, efficient design addresses the increasing need for innovative solutions in agriculture, driven
by the challenges of labor intensive traditional farming. By leveraging advanced technology, the proposed system
demonstrates significant potential in improving farming efficiency and sustainability, particularly in the context of the
growing interest in autonomous agricultural vehicles. The robot's hands-free, autonomous capabilities make it a
valuable asset for farmers, reducing the physical effort and time required for traditional farming practices.

A Novel Approach for Real-Time Audio-to-Sign Language Translation using Naive Bayes classifier and Natural Language Processing Technique

2025-07-01T17:18:17+00:00

Real-time translation from spoken language to sign language, crucial for the deaf and hard of hearing, is difficult employing machine learning advances, this research suggests employing Naive Bayes to instantaneously translate audio input into sign language motions. The initiative provides precise translations between spoken and visual language to improve accessibility and inclusion. The system generates sign language animations from audio inputs using natural language processing and deep learning. Language extraction, tense and context classification, and proficient sign language synthesis are key aspects. The research innovates by using a Naive Bayes model trained on varied spoken language and sign language gesture datasets to ensure robust performance across contexts and user situations. To enhance realism and comprehension, the system integrates a 3D animated avatar to render the translated signs visually. A custom-built gesture mapping mechanism aligns each keyword with its corresponding sign language motion. The tool operates in real-time, supporting both sentence-level and word-level translations. This approach contributes to bridging communication gaps and supports inclusive digital interaction for the hearing-impaired community.

Automated Detection of Image Forgery with Deep Learning

2025-07-01T18:07:22+00:00

The image forgery techniques are used to provide the particular image in the form of computerized pictures with no errors in capturing of digital image information which shows the original image. While compared to normal images the edited images are difficult to find out the forged images. The image should maintain the authenticity and integrity to secure the picture from unauthorized users. In this paper, we have compared the digital image forgery and JPEG is the most common format used by the photographic images and the digital camera devices. These operations are performed in an adobe photo-shop using with the content of image security to restore some digital image with an authenticity and integrity to detect the digital image forgery using active and passive techniques.

An Adaptive Traffic Control System Using Arduino Mega

2025-07-01T18:27:04+00:00

Traffic congestion problem is a phenomenon which contributed huge impact to the transportation system in our country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light assistance system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Arduino Atmega 328 microcontroller to controls the LEDs used in the traffic signals. The use of hazard LED in the system which helps the emergency vehicles to pass the traffic easily. This system will reduce accidents which often happen at the traffic light intersections because of another vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the traffic assistance system for emergency vehicles.

CNN-Based Tomato Leaf Disease Detection With Smart Spraying Mechanism

2025-07-01T18:47:49+00:00

Indian agriculture plays a vital role in crop production and economic development, providing employment to a significant portion of the population. The health of plants is crucial for better yield and profit, requiring continuous monitoring to prevent diseases that can severely impact crop productivity. Traditional disease detection relies on manual observation, which is time-consuming and inefficient. To address this, an automated plant disease identification and prevention system is introduced, integrating IoT-based agricultural monitoring with Machine Learning algorithms. Various IoT sensors, such as thermal, moisture, humidity, and color sensors, help detect plant diseases at an early stage. The system enables timely intervention through automated medicine spraying, improving efficiency and crop health.

Advanced Structural Analysis And Design Optimization Of High- Rise Building Utilizing Building Information Modeling (BIM) Techniques

2025-07-01T19:17:04+00:00

In this study, we will be exploring the advanced structural analysis and design optimization techniques for a G+16 high-rise RCC building using STAAD.Pro V8i SS6. Based on IS codes, the building is evaluated for dead, live, and seismic loads, with manual calculations and software evaluations being utilized to evaluate the data. This involves evaluating the design elements, including stresses on beams and columns, reinforcement detailing, and structural stability. Other important considerations are also given. These findings provide evidence that STAAD.Pro can design high-rise buildings with accuracy, safety, and affordability without requiring manual methods, while also being an excellent substitute for advanced software tools.

Studying The Strength And Durability Properties Of Steel Fiber Reinforced Self -Compacting Concrete

2025-07-01T19:36:43+00:00

This study investigates the strength and durability characteristics of steel fiber reinforced self-compacting concrete (SFRSCC). The primary objective is to evaluate the influence of varying steel fiber contents on the mechanical performance and durability performance of self-compacting concrete. Tests conducted include compressive strength, splitting tensile strength, flexural strength, water absorption, sorptivity, and resistance to chloride ion penetration. The experimental results demonstrate that the inclusion of steel fibers significantly enhances the tensile and flexural strengths, improves the concrete's crack resistance, and reduces permeability, thereby augmenting its durability. The findings suggest that steel fiber reinforcement is an effective method to improve the structural integrity and longevity of self-compacting concrete in various construction applications. The optimal fiber content balancing workability, strength, and durability is also discussed, providing valuable insights for structural engineers and material scientists aiming to design durable, high-performance concrete composites.

Research On The Development Of Eco-Friendly, High-Strength, And Durable Concrete Materials Used In Modern Construction

2025-07-01T19:44:10+00:00

The construction industry is increasingly seeking sustainable solutions to address environmental concerns while meeting the demands for high-performance structural materials. Traditional concrete, although widely used, significantly contributes to carbon emissions and resource depletion. This research explores the development of eco-friendly concrete composites that combine high strength and durability for modern construction applications. Emphasis is placed on incorporating supplementary cementitious materials such as fly ash, slag, and silica fume, as well as innovative eco-friendly binders and admixtures that reduce the reliance on Portland cement. The study evaluates the mechanical properties, durability factors, and environmental impacts through experimental testing, including compressive strength, tensile strength, water absorption, and resistance to chemical attacks. Sustainable approaches such as the utilization of recycled aggregates and biogenic materials are also examined. The findings demonstrate that optimized mixes can achieve comparable or superior performance to conventional concrete, with significant reductions in carbon footprint and resource consumption. The research underscores the potential of eco-friendly, high-strength, and durable concrete as a cornerstone for sustainable infrastructure development, contributing to environmentally responsible construction practices.