Artificial intelligence in agriculture

Article: RT0220 Introduction The raise in cost of cultivation, crop failures due to unpredictable diseases, loss of soil fertility and labour shortage has significant negative impact in agriculture. Increasing in demand and shortage of supply adversely affect the socio economic status. Machine learning and artificial intelligence attain its importance to overcome this strategy. Artificial intelligence is the intelligence exhibited by machines, rather than human or other animals. The action to maximize the success will be carried out by using the intelligent agents which perceives its environment (Russel et al., 2015). Artificial intelligence pave the way towards drones, robotics and sensor based technologies. Certain apps like plantix, ICRISAT sowing app etc, guides farmers throughout the process of, sowing, disease and pest management, harvesting and sale of produce. Drones Drones have the potential to address major challenges. It gives a high tech makeover in agriculture. It is used throughout the life cycle of crop. Crop will be scanned using both visible and near infrared light drone-carried devices can help track changes in plants and indicate their health and alert farmers to diseases. Planting and spraying is faster with drones than traditional machinery. Drone is one platform that allows the field to be sensed multiple times throughout the growing season for identifying the ideal timing for inseason fertilizer application (Thompson et al., 2017). Piloted agricultural aircraft like unmanned aerial vehicles are remotely controlled for crop production. Robotics Robotics is a new trend in agriculture. It is performing various agricultural operations autonomously such as spraying, weed control, fruit picking, and manage individual plants in novel ways. Blue River technology is founded in 2011. It is a California-based start-up combines Biotica Research Today Global population by 2050 is expected to reach more than nine billion. Raise in population may create a huge food demand and to fulfil the food security which will require an increase in agricultural production by 70%. So we have to get more produce from limited land and also by reducing the cost of cultivation. New technologies will be needed to ease the workload on farmers. Field operations will be remotely controlled and automated risk will be identified throughout the crop cycle. This machine learning also develops farmers’ friendly apps to ease the workload of farmers and to improve a wide range of agriculture related risk. 2020 Popular Art ic le (Success Story