Authors :
Bhagyalakshmi V; Sunil Moorti Naik; Apoorva B N; Gowthami M; Preethi N
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/bdekp8mj
DOI :
https://doi.org/10.38124/ijisrt/25may1796
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This project is about developing a grass cutting machine that runs on solar power instead of electricity or fuel.
The main idea is to make a simple, eco-friendly, and cost-effective solution for cutting grass in gardens, parks, and small
farms. The machine uses a solar panel to collect energy from the sun, which is then stored in a rechargeable battery. This
stored energy is used to power the motor and blades of the grass cutter. Since it uses solar energy, it helps reduce pollution
and does not rely on fossil fuels or electricity from the grid. It also saves money in the long run, as there is no need to buy
petrol or pay electricity bills. The machine is easy to operate, lightweight, and can be used in rural or remote areas where
power supply may be limited. The main goal of this project is to promote the use of renewable energy and provide a
practical tool for maintaining green spaces in an environment-friendly way.Grapes are a globally cultivated fruit crop, but
their productivity is significantly affected by various leaf diseases. Early detection and accurate classification of these
diseases are essential for effective management and prevention. This project presents an intelligent system for automated
grape leaf disease classification using deep learning techniques.
Keywords :
Solar Energy; Grass Cutting Machine; Renewable Energy; Eco-friendly Technolog; Deep Learning; Grape Leaf Disease; Image Classification; Smart Agriculture.
References :
- Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556. https://arxiv.org/abs/1409.1556
- Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science,7,1419. https://doi.org/10.3389/fpls.2016.01419
- PlantVillage Dataset. Grape Leaf Disease Images. https://www.kaggle.com/emmarex/plantdisease
- Chollet, F. (2015). Keras: The Python Deep Learning Library. https://keras.io
- Flask Documentation. (n.d.). Flask Web Framework. https://flask.palletsprojects.com
- Brownlee, J. (2019). Deep Learning for Computer Vision. Machine Learning Mastery. https://machinelearningmastery.com
This project is about developing a grass cutting machine that runs on solar power instead of electricity or fuel.
The main idea is to make a simple, eco-friendly, and cost-effective solution for cutting grass in gardens, parks, and small
farms. The machine uses a solar panel to collect energy from the sun, which is then stored in a rechargeable battery. This
stored energy is used to power the motor and blades of the grass cutter. Since it uses solar energy, it helps reduce pollution
and does not rely on fossil fuels or electricity from the grid. It also saves money in the long run, as there is no need to buy
petrol or pay electricity bills. The machine is easy to operate, lightweight, and can be used in rural or remote areas where
power supply may be limited. The main goal of this project is to promote the use of renewable energy and provide a
practical tool for maintaining green spaces in an environment-friendly way.Grapes are a globally cultivated fruit crop, but
their productivity is significantly affected by various leaf diseases. Early detection and accurate classification of these
diseases are essential for effective management and prevention. This project presents an intelligent system for automated
grape leaf disease classification using deep learning techniques.
Keywords :
Solar Energy; Grass Cutting Machine; Renewable Energy; Eco-friendly Technolog; Deep Learning; Grape Leaf Disease; Image Classification; Smart Agriculture.
