AGROFACTS

• Published in: 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) pp. 1 - 6
• Main Authors: Macriga, G. Adiline, B, Gokula Madhavan, B, Kotteeswaran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.12.2023
• Subjects: Agriculture; Biological system modeling; Crops; FLASK; Hardware Interfacing; Machine learning; Machine learning algorithms; Prediction algorithms; Soil
• DOI: 10.1109/ICCEBS58601.2023.10449061

A significant economic engine is agriculture. It is essential for a thriving ecology. A vast variety of agricultural goods are essential to human life in practically every way. Farmers must adapt to climate change while supplying more food grains of higher quality to a growing global population. Knowing the soil's nutritional composition and climatic circumstances will help the farmer choose the best crop to plant in those conditions and increase crop output and growth. This data is to be collected using specialized sensors which will be fed to the pre-trained machine learning model through a hardware interface that offers suggestion of appropriate crops for the data supplied. The model is deployed in a Web application using the FLASK framework which provides interface between the model and the web application. The study has been made on various prediction algorithms to suggest the best crop to obtain its maximum yield. In the agricultural industry, machine learning techniques are employed to increase crop output and quality. The suggestion of an ideal crop can be aided by the use of relevant algorithms using sensed data. In addition, users can send purchase request and they can purchase products such as fertilizers, pesticides, weedicide, manure through website. Vendors can show their products for sale conveniently.