Enhancing Agricultural Productivity Using IoT Based Predictive Analysis and Decision Support Systems
Nilotpal Das, Sk. Moinuddin, Meghna Sarkar, Arafat Mondal
Asian Journal of Advances in Agricultural Research · pp. 56–68 · Published 18 May 2026
10.9734/ajaar/2026/v26i4725Abstract
The Agricultural sector is a great factor that helps to promote the economy of developing countries since a significant percentage of the human population lives off agriculture. However, the potential is not the only threat to the sector, and unpredictable circumstances of nature are certainly among the most important threats. Since there is a necessity to preserve the sustainability of agricultural production, the use of modern technologies to monitor the condition of the soil and products with some level of control has become a priority in the past several years. In this respect, the geospatial technologies such as Geographic Information Systems (GIS) and remote sensing techniques can prove useful. The instruments are highly needed in terms of forecasting crop maturity and crop stand management, as well as proper management of agricultural yields. Besides, agricultural weather information and meteorology of agriculture play a crucial role in the estimation of crop management, improving crop management activities. The testing of such is also encouraged by the usage of various kinds of sensors and modelling methods. Remote sensing can also prove quite useful in the detecting and appraising crop loss due to biotic processes to make prompt decisions about how to make better decisions and implement more effective methods of management. This study is a short overview of the applications and benefits of GIS and remote sensing technologies on the agricultural industry, and their significance in terms of escalating productivity and resistance. With the continued advancement of technology, the development of innovative methodologies and analytical tools is expected to further expand the application of remote sensing and Geographic Information Systems (GIS) within the agricultural sector. These technological innovations are likely to enhance the accuracy and efficiency of monitoring, modelling, and managing agricultural systems, thereby supporting more informed decision-making processes. Furthermore, such progress will contribute significantly to the promotion of sustainable, resilient, and resource-efficient agricultural practices in the context of evolving environmental and socio-economic challenges.
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