Abstract
Smart farming includes various operations like crop yield prediction, soil fertility analysis, crop recommendation, water management, and many activities. Researchers are continuously developing many machine learning models to implement smart farming activities. This paper reviewed various machine learning activities for smart farming. Once a machine learning model is designed and deployed in production systems, the next challenging task is continuously monitoring the model. A monitoring model is required to ensure that models still deliver correct values even underlying conditions changes. This paper reviewed machine learning operations (MLOps) process feasibility for smart farming to provide correct smart farming recommendations when any environmental factors or soil properties change by continuously monitoring the smart farming process by MLOps.
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Akkem, Y., Biswas, S.K., Varanasi, A. (2023). Smart Farming Monitoring Using ML and MLOps. In: Hassanien, A.E., Castillo, O., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. ICICC 2023. Lecture Notes in Networks and Systems, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-99-3315-0_51
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