Paper—Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient… Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Defciency in Rice Plants Using Transfer Learning Models https://doi.org/10.3991/ijim.v16i16.31497 M. Sobhana 1() , Vallabhaneni Raga Sindhuja 2 , Vasireddy Tejaswi 2 , P Durgesh 2 1 Faculty at Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India 2 Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India sobhana@vrsiddhartha.ac.in Abstract—India is an agricultural country, and farming is the most common occupation among Indians. Rice is a vital crop in the agricultural industry. Productivity has been declining for almost a decade. There are several causes for this, including fragmented land holdings, Indian farmer illiteracy, a lack of decision-making capacity in selecting excellent seeds, manure, and irrigational infrastructure. One of the major reasons for rice crop failure is due to malnutri- tion. Rice, maybe in particular, lacking in nutrients such as potassium, nitrogen, and phosphorus. Nutrient defciency detection in crops is necessary to plan further actions to enhance yield. Most studies have relied on the use of transfer learning models for agricultural uses. Ensembling of different transfer learning techniques has the ability to greatly increase the predictive model’s performance. Five transfer learning architectures InceptionV3, Xception, VGG16, Resnet50, and MobileNet are all taken into account, and their different ensemble models are used to perform defciency detection in rice plants where ensembled models performs better when compared to individual models. The ensembled model i.e. InceptionV3 + Xception has achieved an accuracy of 98% when compared to other models and it can be uti- lized in real time situations. The mobile application was created as a user-friendly interface to assist farmers. The accurate diagnosis of these nutritional defciencies like nitrogen, potassium, phosphorus and recommendation of fertilizer to corre- sponding nutrient defciency with the help of mobile application could aid farmers in providing correct plant intervention and to keep track of crop growth. Keywords—ensemble averaging, Inception V3, MobileNet, nutrient defciency, transfer learning 1 Introduction Rice is the most signifcant food crop in Asia, both culturally and economically. For almost 58% of India’s population, agriculture is their primary source of income. There are multiple factors that infuence agriculture production, one of them is 100 http://www.i-jim.org