A Harvesting Identification for Smart Hydroponics Agricultural System Design Using Agro Machine Learning Model

• Published in: 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) pp. 1 - 6
• Main Authors: M, Madiajagan, Atyam, Nageswara Rao, Moharana, Rajeeb Lochan, Gopal, Vidhya, Prabhukumar, S., Singh, Barinderjit
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2023
• Subjects: agricultural machine learning; agriculture; Crops; farmers; fertilizers; harvest; Hydroponics; Machine learning; Potassium; Process control; Rubber; season fails; Sulfur; yield
• DOI: 10.1109/RMKMATE59243.2023.10369986

The technologies on them have also increased in recent developments in the field of agriculture. Many technologies are being introduced, especially for its yield and harvest. Those agricultural products also give good returns when harvested at the right season. Thus the farmers can also do the work required for the next crop. But when the season fails, agricultural products face a variety of dangers. Thus making it impossible to harvest at the right time. An improved agricultural machine learning system has been introduced in this paper. This method is designed to calculate the planting period of the crop, the period of application of required fertilizers and nutrients and the period of harvest and to provide the required instructions to the farmers based on it. This will benefit the farmers and the harvest of the crop will take place in a timely manner.