Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Abstract

As the world's population rises, there will be a greater need for food, which will have repercussions on the environment and on crop yields. Increased production, efficient resource allocation, climate change adaptation, and diminished food waste are the four cornerstones of Agriculture 4.0's vision for the future of farming. Agriculture 4.0 makes use of cutting-edge data systems and Internet technology to acquire, analyze, and organize massive amounts of farming facts such as weather reports, soil conditions, market demands, and land usage to better guide farmers' decisions and boost their bottom lines. As a result, research on agricultural decision support systems for Agriculture 4.0 has gained significant momentum. Crop monitoring and yield forecasting are two applications where remote sensing has proven useful, and these two areas are intrinsically linked to variations in soil, weather, and biophysical and biochemical factors. Multi-and hyper-spectral data, radar, and lidar imaging are just some of the remote tools that could be employed for crop monitoring and yield forecasting. This paper's goal is to examine some of the difficulties that can arise in the future while using agricultural decision-support platforms in the context of Agriculture 4.0. Addressing these identified obstacles may help future researchers create better decisionassistance systems. This research examines the possibilities, benefits, and drawbacks of each method, as well as how well they work in various agricultural settings. Furthermore, these methods are demonstrated in a variety of strategies that can be effectively employed. In this research, we take a look at some remote sensing techniques developed to increase farm profits while minimizing their impact on the natural world. This research shows how remote sensing information can be used to predict crop yields, evaluate plant nutrient needs and soil nutrient levels, calculate plant moisture levels, and manage weed populations, among other applications.

Enhancing Laboratory Experience: A Combination of RFID and Machine Learning Techniques to Track Attendance and Upgrade Hardware

“…The need for cooperation, innovation, flexibility in the face of within-company setbacks, efficiency, and synchronisation amongst a PC organization's Iot and its procurement divisions is especially clear in the context of logistics management [4]. RFID systems-are being utilised more and more in real world Internet of Things applications due to their broad operating range-which can reach as many tens in meters-and gathering data in real-time [5].…”