Smart Detection of Macro Nutrient Deficiency in Soybean Plant using Convolutional Neural Network

Abstract

Background: Legumes play an important role in improving soil quality and nutrition in humans. Soybean is one of the legumes which are rich in protein and oil content. Nutrients deficiency in soybean plant could affect the growth of the plants and might lead to loss in its yield. The developments in modern technologies such as computer vision and deep learning are being leveraged in identifying nutrient deficiencies in soybean plants. Methods: Convolutional neural network with six feature extraction blocks and one classification block is developed to identify macro nutrient deficiency in soybean plants. Images are first collected, pre-processed and labeled. They are then split into training images and testing images in the ratio of 80:20. The proposed convolutional neural network model is trained over training images and the final trained model is tested with testing images. Result: In detecting the deficiency of macro nutrients such as nitrogen, phosphorus and potassium in soybean plants, the testing results of the proposed convolutional neural network architecture achieved an accuracy of 97.43%. Accuracy comparison against existing models such as VGG16, ResNet50 and MobileNetV3 demonstrate that the proposed model effectively identifies nutrient deficiencies in soybean plants. Thus, the proposed system is designed to support farmers in making timely decisions and to contribute to food security by leveraging deep learning techniques.

Plant phenotyping relevance

大豆葉画像から栄養欠乏状態を推定するCNNを開発・評価しており、植物状態の取得・分類手法が研究の中心であるため含める。

Code and data availability

The paper's soybean leaf image dataset (3309 field-captured images) and CNN code are described but no public deposit or availability statement is given. The only public dataset mentioned (ASDID on Zenodo) is cited prior work used as a source of some images, not this paper's own asset. No qualifying paper-specific asset

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