Classification of water stress in cultured Sunagoke moss using deep learning

Hendrawan, Yusuf; Damayanti, Retno; Riza, Dimas Firmanda Al; Hermanto, Mochamad Bagus

doi:10.12928/telkomnika.v19i5.20063

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…The selected models ranged from the simplest architecture (AlexNet and MobileNet V2) to the most complex architecture (GoogLeNet, Inception V3, and ResNet50) in order to evaluate their robustness and efficiencies for crop water stress prediction. Successful application of these models has been reported with accuracies up to 100% for crop abiotic and biotic stress classification in recent studies [ 35 , 36 , 37 ]. Standardized architectures were used in the models for performance comparisons ( Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

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Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress.

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Section: Methodsmentioning

confidence: 99%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

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“…AlexNet addresses the over-fitting problem by using drop-out layers where a connection is dropped during training with a probability of p=0.5. Figure 3 shows the flow chart of the four pre-trained CNN models used in this study (Hendrawan et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Untitled

2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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“…The distribution for training and validation data was 70% training data and 30% validation data which was divided randomly [38]. In addition to the training and validation data, this study also used testing data for a total of 150 image data which were then categorized into 50 image data for the wet class, 50 image data for the semi-dry class, and 50 image data for the optimal dry class [36] [39]. In general, the CNN structure used in this study is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Several CNN methods that have been widely used in the research of food product classification include SqueezeNet [32], AlexNet [33], GoogLeNet [34], and ResNet-50 [35]. In the research of Hendrawan [36], it was proven that pre-trained CNN using SqueezeNet, AlexNet, GoogLeNet, and ResNet-50 had a high performance for modeling and classifying the water content of agricultural materials with the highest accuracy value reaching 94.15% on the testing data set.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network to Detect the Optimal Water Content of Cassava Chips During the Drying Process

Hendrawan¹,

Rohmatulloh²,

Ilmi³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Cassava chips are used as raw materials to manufacture modified cassava flour. To produce high-quality modified cassava flour, a drying process for cassava chips is required to produce optimal water content in the range of 15-18% wb. This study aims to detect the optimal water content of cassava chips during the drying process in a hybrid hot-air tray dryer with computer vision using a convolutional neural network. Three categories of cassava chips' water content during the drying process are wet (water content of 55-70% wb), semi-dry (20-40% wb), and optimal dry (15-18% wb). In this study, the performance of four types of the pre-trained convolutional neural network, i.e., AlexNet, GoogLeNet, ResNet-50, and SqueezeNet, were compared by using different optimizers (SGDm, Adam, and RMSProp) and different learning rate values, 0.00005 and 0.0001, resulting in 24 types of experimental design. The results showed 12 convolutional neural network models with perfect validation accuracy. AlexNet with the SGDm optimizer and learning rate of 0.00005 was determined as the best model because of its stable training iteration process that experienced no fluctuations, perfect validation accuracy, specifically 100%, as well as perfect testing accuracy was 100%, and fastest training and validation process time, notably 32 minutes. This best convolutional neural network model will later be used to develop a rapid, realtime, and accurate hybrid hot-air tray dryer with computer vision to maintain cassava chip products with optimal water content.

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Classification of water stress in cultured Sunagoke moss using deep learning

Cited by 16 publications

References 36 publications

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

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Convolutional Neural Network to Detect the Optimal Water Content of Cassava Chips During the Drying Process

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