Smart farming using iot for efficient crop growth
In general. automated farming systems make decisions based on static models built from the properties of the plant. in the contrast, irrigation decisions in our suggested method are dynamically changing environmental conditions. the model"s learning process reveals the mathematical links between the environmental factors employed in the determining the irrigation habit and gradually improves its learning techniques as irrigation data accumulates int the model. to analyze overall system overall system performance, we constructed a test environment for the sensor edge, mobile client, and decision service in the cloud.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Internet of Things-Based Smart Precision Farming in Soilless Agriculture:Opportunities and Challenges for Global Food Security
The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. So…
Decision MakingOptimal Control for Indoor Vertical Farms Based on Crop Growth
Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security. This closed and highly controlled system allows the plants to grow in optimal con…
Scene and Environment Monitoring Using Aerial Imagery and Deep Learning
Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in…
Decision MakingDeep LearningGeneral ClassificationSegmentationHierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription m…
Crop Yield PredictionFederated LearningEnhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture
The agricultural sector is undergoing a transformation with the integration of advanced technologies, particularly in data-driven decision-making. This work proposes a federated learning framework for smart farming, aimi…
Federated LearningTransfer Learning