Applications of computer vision and drone technology in smart agriculture-present status and future prospects

The challenges imposed by the scarcity of resources, undulating terrain, peak labour shortage and climate change has propelled the growth of automated technologies in agriculture. The computer vision system can be employed for disease detection, crop yield estimation, grading, weed control, spraying and harvesting. The emergence of technologies like neural network, deep learning has improved the performance of computer vision-based system. The basic requirement of acquiring the data from the field is solved by sensors, internet of things (IoT) and drones. The sensors collect the data from the field and integrate it through a common platform and process the data for valuable information through artificial intelligence tools. The drones provide high quality images and data from remote distance. High quality images remotely and help to collect the data. The images are then subjected to processing though image processing algorithm-based software's to extract the information, which serves as the fodder to take informed decisions. The involvement of modern technologies of computer vision, deep learning, internet of things, drones, artificial intelligence can help to reduce the dependence on manual labour, improve efficiency of operations and leverage towards smart agriculture.