AGRO-Cloud Model and Smart Algorithm to Increase Agriculture Production to Improve Agriculture Quality

Avdesh Kumar Sharma^* and Abhishek Singh Rathore

Department of Computer Science and Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore

Corresponding Author E-mail:avdeshsharma@svvv.edu.in

DOI : http://dx.doi.org/10.12944/CARJ.12.3.14

Abstract:

Smart Agriculture is a revolutionary approach to farming that aims to increase crop yields, optimize resource usage, and reduce costs, through the use of technology the design and implementation of an AGRO-Cloud Model for crop yield prediction using hybrid deep learning. The proposed system aims to improve crop yield prediction accuracy and facilitate decision-making for farmers. The system utilizes a hybrid deep learning approach that associates the (CNNs) convolutional neural networks and (LSTM) long short-term memory networks to process multi-sensor data, including soil, moisture data, weather data, and data of crop growth. LSTMs are used to capture temporal dependencies in the input data, while CNNs are utilized to extract spatial features. The system is implemented on a cloud platform, allowing farmers to access the system from anywhere using a web-based interface. The system provides real-time crop yield prediction and alerts farmers to potential risks such as pests, disease, and adverse weather conditions. The system also provides data visualization tools that enable farmers to monitor the growth of their crops and make informed decisions about fertilization, crop management practices, and irrigation. Experimental results show that the proposed hybrid deep learning approach outperforms traditional machine learning methods for crop yield prediction, achieving a prediction accuracy of over 90%. The proposed model can increase agriculture production to improve the quality and profitability of farming operations and contribute to sustainable agriculture practices.

Keywords:

AGRO-Cloud; Cloud-Based Smart; Deep learning; Docker Containers; Smart farming

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