Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1

Applications of Emerging Smart Technologies in Farming Systems: A Review © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 53 ( )G Year 2023 a mobile phone for soil/tank sensors. In India, farmers already use a device named Nano Ganesh. ( Ostwal Santosh 2015 ). Mobile Technology using Artificial Intelligence, can help an average Indian farmer to get information regarding forecasting and to predict the weather, crop production, etc., in a timely and precise manner. Scientists at Indian Institute of Farming Systems Research are working on developing an Artificial Neural Network (ANN) -based mobile application (Fig. 5) to help farmers and researchers in the selection of crop variety, planting schedule, disease, pest, weed, weather information, yield forecast, Soil testing labs, and prevailing Govt schemes related to farming, Contact to Kisan Helpline, etc. The application is being developed using the Android Studio platform, Java language, XML, and SQLite database. The neural network (Back- propagation algorithm) is being used to create a feature of yield prediction network that can contain three layers of neurons: an input layer, a hidden layer (which is optional), and an output layer. So far, two hidden layers are employed with four neurons in each hidden layer. The final number of hidden layers and number of neurons will be determined by conducting fair numbers of training schedules on data sets Therefore, resource-poor farmers can be benefitted more from the help of ICT (Information & Communication Technology)/Artificial intelligence. Fig. 5: Smart Phone Application in Mango Crop Management e) Artificial Intelligence, Internet of Things, and Automation In AI and IoT-based smart farming, a system is built for monitoring the crop field with the help of sensors like light, humidity, temperature, soil moisture, etc. The agriculture farmers can monitor the field conditions from anywhere. IoT-based farming is highly efficient when compared with the conventional approach. Most promising AI technologies that transform the agriculture sector, as discussed below. f) Crop and Soil Monitoring AI can analyze and interpret data on image perception to track crop health (Fig. 6) and predict production. crop malnutrition may be detected considerably quicker than in humans. In addition, AI models can advise farmers about specific problem regions, allowing them to take prompt action. (Pradeep N. et al. 2020). Crop health assessment and early detection of crop infestations are critical in ensuring good agricultural productivity. Climate change, nutrient deficiencies, weed, insect, fungal infestations, and other challenges must be detected early enough to enable farmers to mitigate their effects. Fig. 6: Crop assessment & Field Spray by Drone