The agricultural sector has witnessed significant technological transformations over the last few decades. The state-of-the-art technologies are transforming the traditional agriculture models into digital agriculture. From these technologies, conventional agriculture has evolved and shifted towards a smart agriculture system. In a smart agriculture system, farmers can collect and analyze the collected data to fertilize and tend their crops. The smart agriculture system provides economical and more accurate ways to predict and protect crop growth. The incorporation of these technologies digitalizes the agricultural industry by increasing profits, reducing waste, improving efficiency, and becoming sustainable. This chapter aims to study the state-of-the-art technologies used in the agriculture sector and proposes a smart agriculture model using these technologies.
The agricultural sector has witnessed significant technological transformations over the last few decades. The state-of-the-art technologies are transforming the traditional agriculture models into digital agriculture. From these technologies, conventional agriculture has evolved and shifted towards a smart agriculture system. In a smart agriculture system, farmers can collect and analyze the collected data to fertilize and tend their crops. The smart agriculture system provides economical and more accurate ways to predict and protect crop growth. The incorporation of these technologies digitalizes the agricultural industry by increasing profits, reducing waste, improving efficiency, and becoming sustainable. This chapter aims to study the state-of-the-art technologies used in the agriculture sector and proposes a smart agriculture model using these technologies.
Internet of things solutions with machine learning capabilities is a hot research area in industries, including agriculture. They can be used for data analysis and further forecasting the big data and intelligent applications in farming. In traditional farming, the main obstacles are disease prediction, automatic irrigation, energy harvesting, and constant monitoring. Today, farmers' cultivation of their crops has changed by introducing automated harvesters, drones, autonomous tractors, sowing, and weeding. Smart farming with ML-enabled IoT systems can improve crop harvesting decisions. The main topic of this chapter is to provide an ML-enabled IoT solution for smart agriculture. The MIoT solution in agriculture allows farmers to use predictive analytics to help them make better harvesting decisions. Designing a MIoT system for smart agriculture can assist farmers in improving yields, planning more effective irrigation, and making harvest forecasts by monitoring essential data like humidity, air temperature, and soil quality via remote sensors.
The present work deals with the detailed analysis of various unsafe acts by the workforce, which gives the major impact on unsafe conditions of the workspace, which further results in different range of injuries and accidents in the manufacturing industry. The current scenario of the industry is initiated with various safety precautions implemented by our safety professionals. As a result, the number of injuries are reduced in a very meager amount, but the goal of making it down to zero cannot be achieved. In order to achieve the goal of a safety professional, this study presents clear observation by answering the questions why, when where and how loop-holes are created for the happening of unsafe acts. The study specially aims at controlling specific unsafe acts of workers by the elimination of procedural working condition while handling the machineries on various situations, predominantly during maintenance. This analysis gives us clear-cut picture on unsafe acts of the work force during maintenance is directly proportional to the unsafe conditions of work space.
Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever way to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and analyzing big data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications.
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