ABSTRACT
Agriculture plays a vital role in the economic development of Asian countries. Most importantly, it is the main livelihood of people who live in this part of the world. Increasing population density and anthropogenic climate change bring many challenges to the sustainable development of the agriculture sector in this region. Increasing pests and disease, insufficient irrigation and drainage facilities, inadequate post-harvest technologies, and lack of advanced technologies in crop and yield monitoring were identified as key agriculture-related issues in Asia. Reduction in the quality of the crop, biotic and abiotic-related yield losses, high cost of production, lower resource use efficiencies, and environmental impacts due to inappropriate chemical usage are the subsequent issues related to the aforementioned challenges. To address these issues many researchers have been making much effort. From numerous agricultural perspectives, developing transformative techniques with precision agriculture is an extraordinary achievement and with that concept, artificial intelligence (AI) plays a major role while providing better solutions to agriculture-related problems to agriculturists through several systems such as machine learning techniques, artificial neural networks (ANN), convolutional neural networks (CNN), etc., mainly in the Asian countries these days. With the usage of the aforementioned AI-based innovations, farmers can increase the quality and profitability of farming activities by gaining high resource use efficiency and sustainability. Advancements in applying AI in Asian countries are increasing day by day with different forms of techniques such as agriculture drones with hyperspectral and multispectral sensors, agriculture robots, and modified sensors. In addressing sustainable concerns of farming decisions in commercial agriculture, AI applications including machine learning techniques provide a vast amount of critical data along with the main elements of emerging precision agriculture. To improve the quality and quantity of the agricultural output, AI systems help to monitor irrigation and drainage issues, pH, moisture, nutrient availability, electrical conductivity (EC), and cation exchange capacity (CEC) like soil conditions and environmental conditions as well to gain economic benefits of agriculture.
