Identification of Nutrition’s Deficiency in Plant and Prediction of Nutrition Requirement Using Image Processing

Patel, Ashish; Swaminarayan, Priya; Patel, Mukta

doi:10.1007/978-981-15-9689-6_50

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Thus, automated mechanisms are required to detect vascular plants with nutrient deficiencies precisely and timely. Image-based plant nutrient deficiency identification is a promising and efficient solution as it is a non-invasive, efficient, and accurate method that can be applied over large fields [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Kolhar,

Jagtap,

Shastri

2024

IJCDS

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Nutrients are vital in ensuring expected crop growth and yield quality. Accurate identification of nutrient deficiencies in plants is essential to provide appropriate supplements of fertilizers. Manual inspection of symptoms and identifying nutrient deficiencies is a tiresome task requiring higher expertise. This paper aims to design and develop a computationally efficient deep-learning model to classify plant nutrient deficiencies accurately. This paper presents an image-based deep-learning framework for nutrient deficiency identification. Three deep learning models, namely the Xception model, vision transformer, and multi-layer perceptron-based (MLP) mixer model, were trained to identify nitrogen (N), phosphorous (P), and potassium (K) deficiencies in rice plants from red-green-blue (RGB) images. The model performance is tested on nutrient deficiency symptoms in rice plants dataset available publicly on Kaggle. All three models achieved nutrient deficiency classification accuracy greater than 92%. The Xception model achieved the highest average accuracy of 95.14% at the cost of approximately 1.2 million total trainable parameters, much less than the vision transformer and MLP mixer model. The Xception model performs better than the other two models in classifying nutrient deficiencies with the least number of total trainable parameters. In the future, these neural networks can be trained and extended to accurately detect and segment nutrient-deficient crop areas in large fields to supply precise fertilizer supplements.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Kolhar,

Jagtap,

Shastri

2024

IJCDS

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“…This has a series of economic and environmental impacts, such as preventing damage and yield loss, less financial loss, reduced food, etc. Agricultural experts struggle to diagnose the nutrient deficiencies because agricultural production sites are dispersed and many nutrient deficiencies are spread across a large region [9,10].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Sharma¹,

Nath

Sharma

et al. 2022

Electronics

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Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

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An ensemble transfer learning for nutrient deficiency identification and yield-loss prediction in crop

Venkatesh,

Naik

2024

Multimed Tools Appl

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Identification of Nutrition’s Deficiency in Plant and Prediction of Nutrition Requirement Using Image Processing

Cited by 4 publications

References 7 publications

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

An ensemble transfer learning for nutrient deficiency identification and yield-loss prediction in crop

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