Detection of nutrient deficiencies in banana plants using deep learning

Guerrero, Renato; Renteros, Bruno; Castaneda, Renato; Villanueva, Alejandro; Belupu, Ivan

doi:10.1109/icaacca51523.2021.9465311

Cited by 15 publications

(16 citation statements)

References 15 publications

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“…One example of data augmentation is processing rotation and blurring [41]. The study [42], [43] has proven that image augmentation so that each class has the same amount of data increases the accuracy of the model.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Research [26] used the HIS/HSL color model. Research xx identified diseases on banana leaves by comparing the YUV, CIELAB, YCbCr, and HSV color space models [27]. The result is YUV color space followed by a final conversion to RGB shows the best result.…”

Section: Related Workmentioning

confidence: 99%

“…The YUV model defines one luminance component (Y), that means physical linear-space brightness, and two chrominance components called U (blue projection) and V (red projection) respectively. It can be used to convert to and from the RGB model, and with different color spaces [27]. The formula convert RGB color model to YUV shown in Equation ( 14) - (16).…”

Section: Yuvmentioning

confidence: 99%

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

Advances in Biological Sciences Research

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Chili is a horticultural crop that has high economic value in Indonesia. The productivity level of chili in the country is not proportional to the level of consumption, one of the causes is malnutrition. Each plant requires different amounts of macronutrients and micronutrients to support plant growth and development. Chili plants that lack or excess macronutrients show different visual symptoms. Digital Image Processing is a non-destructive method that is useful for determining plant health conditions based on visual symptoms of chili leaves. The combination of digital image processing and learning methods such as the Support Vector Machine (SVM) helps classify the types of macronutrient deficiencies in order to obtain a nutrient solution. In this study, there are several stages in determining macronutrient deficiencies in chili plants, namely image acquisition, pre-processing, feature extraction, to classification using SVM with several kernels. Based on the experimental results in this study, the SVM method can help modern farmers to determine the health condition of plants non-destructively with 97.76% accuracy using a Polynomial kernel. Applying this system to an intelligent farming system is expected to support the realization of precision agriculture in Indonesia.

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“…In recent years, many researchers have studied transfer learning. , Wan et al developed a new transfer learning method by coupling transfer component analysis with support vector regression (TCA-SVR) to transfer LNC (leaf nitrogen concentration) assessment models across different plant species. Guerrero et al used image recognition of banana leaves using convolutional neural networks trained by transfer learning and fine-tuning to determine the absence of nutrients in banana leaves. The cost of obtaining a large number of different pear leaf deficiency samples is too high, so transfer learning is very important for modeling of pear leaf deficiency by NIR spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

“…In general, if one neural network can extract features in a specific dataset, transfer learning can strengthen its generalization to similar tasks without requiring too much data in the other datasets. In recent years, many researchers have studied transfer learning. , Wan et al developed a new transfer learning method by coupling transfer component analysis with support vector regression (TCA-SVR) to transfer LNC (leaf nitrogen concentration) assessment models across different plant species. Guerrero et al used image recognition of banana leaves using convolutional neural networks trained by transfer learning and fine-tuning to determine the absence of nutrients in banana leaves.…”

Section: Introductionmentioning

confidence: 99%

A Novel Tran_NAS Method for the Identification of Fe- and Mg-Deficient Pear Leaves from N- and P-Deficient Pear Leaf Data

Jin

Wang

et al. 2022

ACS Omega

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Trace element deficiency diagnosis plays a critical role in pear cultivation. However, high-quality diagnostic models are challenging to investigate, making it difficult to collect samples. Therefore, this manuscript developed a novel transfer learning method, named Tran_NAS, with a finetuning neural network that uses a neural architecture search (NAS) to transfer learning from nitrogen (N) and phosphorus (P) to iron (Fe) and magnesium (Mg) to diagnose pear leaf element deficiencies. The best accuracy of the transferred NAS model is 89.12%, which is 11% more than that of the model without the transfer of trace element-deficient samples. Meanwhile, Tran_NAS also has better performance on source datasets after comparing with different proportions of training sets. Finally, this manuscript summarizes the transfer model coincident characteristics, including the methods of batch normalization (BN) and dropout layers, which make the model more generalizable. This manuscript applies a symmetric homogeneous feature-based transfer learning method on NAS that is designed explicitly for near-infrared (NIR) data collected from nutrient-deficient pear leaves. The novel transfer learning method would be more effective for the micro-NIR spectrum of the nondestructive diagnosis.

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Detection of nutrient deficiencies in banana plants using deep learning

Cited by 15 publications

References 15 publications

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

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

Classification of Macronutrient Deficiency in Chili Leaves using Support Vector Machine

A Novel Tran_NAS Method for the Identification of Fe- and Mg-Deficient Pear Leaves from N- and P-Deficient Pear Leaf Data

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