Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models

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 malnutrition. Rice, maybe in particular, lacking in nutrients such as potassium, nitrogen, and phosphorus. Nutrient deficiency 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 deficiency detection in rice plants. The mobile application was created as a user-friendly interface to assist farmers. The accurate diagnosis of these nutritional deficiencies and recommendation of fertilizer could aid farmers in providing correct plant intervention.

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