This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Rohan Chandrashekhar Nashikkar,
Aniket Raghav Ojha,
3Shubham Jitendra Sankpal,
Ruchir Dinesh Rao,
- Student, Department of Information Technology, South Indian Education Society (SIES) GST, Navi Mumbai,, Maharashtra,, India.
- Student, Department of Information Technology, South Indian Education Society (SIES) GST, Navi Mumbai,, Maharashtra,, India.
- Student, Department of Information Technology, South Indian Education Society (SIES) GST, Navi Mumbai,, Maharashtra,, India
- Student, Department of Information Technology, South Indian Education Society (SIES) GST, Navi Mumbai,, Maharashtra,, India
Abstract
This study presents a cutting-edge application that will transform home gardening and agriculture practices using machine learning (ML) approaches. The main goal is to provide data-driven insights to home gardeners and farmers, enabling them to implement efficient and sustainable farming practices. Crop disease detection, fertiliser recommendation, and a community section for user engagement comprise the three main elements that make up the system’s architecture. The Crop Disease Detection module analyses photos, environmental data, and past disease trends to identify possible diseases in crops using sophisticated machine learning models. Early identification enables prompt actions, reducing crop losses and guaranteeing environmentally friendly agricultural methods. The Fertiliser Recommendation module uses machine learning (ML) algorithms to examine crop type, soil properties, and historical data to generate customised and ideal fertiliser recommendations. By guaranteeing that crops receive the precise nutrients they require, this encourages precision farming by boosting growth and production while reducing resource waste. Users can interact, exchange experiences, and seek guidance in a collaborative environment that is fostered by the Community Section, forming a network of support. This feature enhances knowledge exchange and provides a platform for users to collectively address challenges and share best practices. The integration of these modules creates a comprehensive tool that adapts to specific environmental conditions, plant varieties, and user preferences, offering tailored recommendations for optimal crop cultivation. The system collects real-time data through various sensors, processes this data to derive meaningful features, and employs both supervised and unsupervised ML algorithms for accurate predictions and recommendations. User inputs further personalize the experience, ensuring the tool is adaptable to the unique needs of each user. This paper demonstrates the efficacy of the proposed tool through rigorous testing and comparative analysis, highlighting its potential to significantly enhance productivity and resource efficiency in both home gardening and larger agricultural settings. The findings indicate that the tool not only improves crop health and yield but also fosters a sense of community among users, contributing to the broader goals of sustainable agriculture.
Keywords: Agriculture, Harvesting, Crop Disease, Fertilizer, Community, Social Media, Machine Learning, NPK, Farmers.
[This article belongs to International Journal of Electrical and Communication Engineering Technology (ijecet)]
Rohan Chandrashekhar Nashikkar, Aniket Raghav Ojha, 3Shubham Jitendra Sankpal, Ruchir Dinesh Rao. Harvestify: ML Based Tool for Home Gardening and Farming. International Journal of Electrical and Communication Engineering Technology. 2024; 02(02):11-20.
Rohan Chandrashekhar Nashikkar, Aniket Raghav Ojha, 3Shubham Jitendra Sankpal, Ruchir Dinesh Rao. Harvestify: ML Based Tool for Home Gardening and Farming. International Journal of Electrical and Communication Engineering Technology. 2024; 02(02):11-20. Available from: https://journals.stmjournals.com/ijecet/article=2024/view=185138
References
- Hussain, S. Sarfraz and S. Javed, “A Systematic Review on Crop-Yield Prediction through Unmanned Aerial Vehicles,” 2021 16th International Conference on Emerging Technologies (ICET), Islamabad, Pakistan, 2021, pp. 1-9, doi: 10.1109/ICET54505.2021.9689838.
- Suciu, I. Pop, A. Pasat, S. Calescu, R. Vatasoiu and I. Suciu, “Digital Solutions for Smart Food Supply Chain,” 2021 IEEE 27th International Symposium for Design and Technology in Electronic Packaging (SIITME), Timisoara, Romania, 2021, pp. 378-381, doi: 10.1109/SIITME53254.2021.9663672.
- Pyingkodi, K. Thenmozhi, M. Karthikeyan, T. Kalpana, S. Palarimath and G. B. A. Kumar, “IoT based Soil Nutrients Analysis and Monitoring System for Smart Agriculture,” 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2022, pp. 489-494, doi: 10.1109/ICESC54411.2022.9885371.
- Saeed, A. Raza, A. H. Qureshi and M. Haroon Yousaf, “A Multi-Crop Disease Detection and Classification Approach using CNN,” 2021 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan, 2021, pp. 1-6, doi: 10.1109/ICRAI54018.2021.9651409.
- Rakesh D, V. Vardhan, B. B. Vasantha and G. Sai Krishna, “Crop Recommendation and Prediction System,” 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 1244-1248, doi: 10.1109/ICACCS57279.2023.10113081.
- Pradeep, T. D. V. Rayen, A. Pushpalatha and P. K. Rani, “Effective Crop Yield Prediction Using Gradient Boosting To Improve Agricultural Outcomes,” 2023 International Conference on Networking and Communications (ICNWC), Chennai, India, 2023, pp. 1-6, doi: 10.1109/ICNWC57852.2023.10127269.
- Harika, G. Sandhyarani, D. Sagar and G. V. S. Reddy, “Image-based Black Gram Crop Disease Detection,” 2023 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, 2023, pp. 529-533, doi: 10.1109/ICICT57646.2023.10134027.
- Rawankar et al., “Detection of N, P, K fertilizers in agricultural soil with NIR laser absorption technique,” 2018 3rd International Conference on Microwave and Photonics (ICMAP), Dhanbad, India, 2018, pp. 1-2, doi: 10.1109/ICMAP.2018.8354625.
- Hammad Shahab, Muhammad Iqbal, Ahmed Sohaib, Farid Ullah Khan, Mohsin Waqas. IoT-based agriculture management techniques for sustainable farming: A comprehensive review. Computers and Electronics in Agriculture. Volume 220, May 2024, 108851
- Isaac Kofi Nti, Adib Zaman, Owusu Nyarko-Boateng, Adebayo Felix Adekoya, Frimpong Keyeremeh. A predictive analytics model for crop suitability and productivity with tree-based ensemble learning. Decision Analytics Journal. Volume 8, September 2023, 100311
- Chowdhury and A. Ahmad, “Gardener: A Gardening Assistance Mobile Application With Plant Details, Plant Shopping, Expert Contact, Alert, and Plantation Tips Facilities,” 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2023, pp. 1-6, doi: 10.1109/CSITSS60515.2023.10334114.
- Lova Raju K, Vijayaraghavan V. IoT-AgriSens: A LoRa-Based Smart Agriculture Monitoring and Decision-Making System with Amalgamation of IoT and Cloud- Enabled Services, 27 September 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3373849/v1]
- Thilakarathne, Navod Neranjan, Muhammad Saifullah Abu Bakar, Pg Emerolylariffion Abas, and Hayati Yassin. 2022. “A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming” Sensors22, no. 16: 6299. https://doi.org/10.3390/s22166299
Volume | 02 |
Issue | 02 |
Received | 08/08/2024 |
Accepted | 28/08/2024 |
Published | 23/11/2024 |