ENHANCING CROP HEALTH THROUGH DIGITAL TWIN FOR DISEASE MONITORING AND NUTRIENT BALANCE
Sobhana Mummaneni
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0000-0001-5938-5740
Tribhuvana Sree Sappa
tribhuvanasree@gmail.comVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0009-8594-429X
Venkata Gayathri Devi Katakam
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0002-3492-4386
Abstract
Digital Twins is a digital replica of a physical object to observe its real-time performance, gather data, and recommend corrective actions if required to enhance its performance. This fascinating technological idea is now reaching the agriculture fields to transform farming, by creating digital twins of entire farms. This initiative presents an innovative strategy to enhance crop health and yield by creating a digital twin for paddy fields. The aim is to enable early detection of nutrient deficiencies and leaf blast disease, leading to a transformation in agriculture. Creating virtual replicas of plants and fields, the digital twin harnesses real-time data and advanced analytics to transform the way agricultural systems are managed. By integrating remote sensing, data analytics, and various Internet of Things devices like pH, nitrous, potassium, and phosphorus sensors, coupled with a gateway system, the digital twin provides real-time monitoring and analysis of crop health and nutrient levels. Employing advanced machine learning algorithms, notably Convolutional Neural Networks ensures precise and early detection of nutrient deficiencies and crop diseases. This ground-breaking technology provides timely alerts and actionable insights to farmers, enabling proactive decision-making for optimal crop management. This farmland digital twin represents a transformative approach towards agricultural sustainability and enhancing productivity.
Keywords:
agricultural sustainability, convolution neural networks, digital twin, internet of things, nutrient deficiency detectionReferences
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Authors
Sobhana MummaneniVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0001-5938-5740
Authors
Tribhuvana Sree Sappatribhuvanasree@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0009-8594-429X
Authors
Venkata Gayathri Devi KatakamVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0002-3492-4386
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