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

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

Ensemble Averaging

Inception V3

MobileNet

Nutrient Deficiency

Transfer Learning.

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.

M, Sobhana

Vallabhaneni, Raga Sindhuja

Vasireddy, Tejaswi

Polavarpu, Durgesh

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 16 (2022); pp. 100-112

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-08-31

Copyright (c) 2022 Sobhana M, Raga Sindhuja Vallabhaneni, Tejaswi Vasireddy, Durgesh Polavarpu

https://creativecommons.org/licenses/by/4.0

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eng

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info:eu-repo/semantics/publishedVersion

Peer-reviewed Article