Deep Learning Detected Nutrient Deficiency in Chili Plant

Bahtiar, Arief Rais; Pranowo, Pranowo; Santoso, Albertus Joko; Juhariah, Jujuk

doi:10.1109/icoict49345.2020.9166224

Cited by 27 publications

(15 citation statements)

References 20 publications

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“…Assessing leaf nutrient concentrations of citrus trees is important for determining the nutrient status of the plants, which can fluctuate depending on the developmental stage of the plant, environmental conditions and pest/disease pressure. Regular nutrient analysis is important in crop production to identify and correct potential nutrient deficiencies and prevent production losses (Shaw et al, 2002;Kadyampakeni & Morgan, 2020;Bahtiar et al, 2020). Rather than collecting leaves manually and subjecting them to costly chemical analysis procedures, combining UAV imaging (spectral leaf reflectance measurements) and AI is less laborious, safer for the environment and more cost-efficient.…”

Section: Discussionmentioning

confidence: 99%

Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

et al. 2021

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Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus field trials, located in Highlands County and in Polk County, Florida, USA. In each location, trials contained either ‘Hamlin’ or ‘Valencia’ sweet orange scion grafted on more than 30 different rootstocks. Leaves were collected and analyzed in the laboratory to determine macro- and micronutrient concentration using traditional chemical methods. Spectral data from tree canopies were obtained in five different bands (red, green, blue, red edge and near-infrared wavelengths) using a UAV equipped with a multispectral camera. The estimation model was developed using a gradient boosting regression tree and evaluated using several metrics including mean absolute percentage error (MAPE), root mean square error, MAPE-coefficient of variance (CV) ratio and difference plot. This novel model determined macronutrients (nitrogen, phosphorus, potassium, magnesium, calcium and sulfur) with high precision (less than 9% and 17% average error for the ‘Hamlin’ and ‘Valencia’ trials, respectively) and micro-nutrients with moderate precision (less than 16% and 30% average error for ‘Hamlin’ and ‘Valencia’ trials, respectively). Overall, this UAV- and AI-based methodology was efficient to determine nutrient concentrations and generate nutrient maps in commercial citrus orchards and could be applied to other crop species.

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Section: Discussionmentioning

confidence: 99%

Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

et al. 2021

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“…During prediction of four levels of N deficiency in rice and NPK deficiency in oilseed rape, pre-trained CNN models were combined with SVM or time series model producing best results in the range of 99.84% and 95% [26,27]. Although CNNs have shown their satisfactory performance in the field of nutrient deficiency identification in plants, region based CNN lacked detailed classification when applied in Chilli plants [28]. The pre-trained CNN model (MobileNetV2) can be adjusted to show a statistically significant increase in cassava leaf disease identification accuracy on lower-quality testing images [29].…”

Section: Related Workmentioning

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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“…The chili plant is a high economic value of the horticultural plant in Indonesia. However, the production level is lower than the consumption level, with inflation of 0.20% to 0.55% in 2019 [1]. The thing that causes low production is limited land due to the transition of the farming areas to settlements.…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

IJACSA

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Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a nondestructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia.

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Deep Learning Detected Nutrient Deficiency in Chili Plant

Abstract: His research interests include computer vision, pattern recognition, multimedia information recognition, and ITS applications. He is a fellow of the IEEE, a fellow of the IPSJ, and a fellow of the IEICE.

Cited by 27 publications

References 20 publications

Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

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

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

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