Harvestify: ML Based Tool for Home Gardening and Farming - Rohan Chandrashekhar Nashikkar; Aniket Raghav Ojha; Shubham Jitendra Sankpal; Ruchir Dinesh Rao (International Journal of Electrical and Communication Engineering Technology, 2024)
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; Crop Disease; Farmers; Fertilizer; Harvesting; Machine Learning; NPK; community; social media