Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network

Sethy, Prabira Kumar; Barpanda, Nalini Kanta; Rath, Amiya Kumar; Behera, Santi Kumari

doi:10.1007/s12652-020-01938-8

Cited by 79 publications

(28 citation statements)

References 29 publications

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“…In addition, we compared the four DCNN models tested in this study with two traditional machine learning methods, color feature with SVM and HOG (Histogram of Oriented Gradient) with SVM [ 39 , 54 ]. The color feature was read from the images directly and then trained with an SVM classifier (implementation was based on scikit-learn library); the HOG extraction process can be divided into 5 parts: detection window, normalized image, calculated gradient, statistical histogram and normalized gradient histogram, and obtained HOG feature vector.…”

Section: Resultsmentioning

confidence: 99%

“…Since the dataset contains various deficiency periods of plant leaves, it is difficult to classify different types of nutrient deficiencies. Recently, Sethy et al [ 39 ] applied pretrained DCNNs with an SVM classifier to identify four levels of nitrogen deficiency in rice and achieved an accuracy of 99.8%. This encourages us to explore the ability of DCNNs to classify more elements with different deficiency phases in rice.…”

Section: Related Workmentioning

confidence: 99%

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Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

Guo

Zhu

et al. 2020

Computational Intelligence and Neuroscience

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Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

Guo

Zhu

et al. 2020

Computational Intelligence and Neuroscience

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“…Remotely sensed, vegetation indices and climate data are commonly used to predict paddy rice yield estimation [34], [35], [48], [76], [77], [109] and to monitor paddy rice growth [63], [73], [117] using artificial neural networks and its variants and also linear regression approaches. In addition to that, hyperspectral and high-resolution images have been used to accurately and affectively monitor paddy rice disease [40], [41], [87], [88], [119] and assessing quality of paddy rice [93], [104], [105] by using deep learning algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Next, drone based data include all imageries captured using the drone technology. The high resolution images captured using drone can be used to estimate the paddy rice yield [42], [84]- [86], monitor paddy rice disease [37], [39], [87]- [92], classify paddy rice samples [46], [47], [93]- [101] and also assess the quality of paddy rice [102]- [104]. For instance, a near real-time deep learning approach for detecting rice phenology has also been designed based on high resolutions images taken by using drones [86].…”

Section: ) Drone Based Datamentioning

confidence: 99%

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Overview of Edge Intelligence and Blockchain

et al. 2022

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Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network

Cited by 79 publications

References 29 publications

Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Overview of Edge Intelligence and Blockchain

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