Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study

Zakaluk, R.; Ranjan, R. Sri

doi:10.1007/s11540-007-9021-x

Cited by 56 publications

(12 citation statements)

References 35 publications

(31 reference statements)

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“…Potato has been typified as an isohydric crop (Obidiegwu et al 2015) due to its stomatal closure sensitiveness (Vos and Oyarzún 1987) to soil water deficit maintaining leaf water potential through abscisic acid mediation (Liu et al 2005). This strong stomatal closure characteristic reported for clear environments has been used to propose in this crop the monitoring of leaf (commonly assessed at dawn) or stem (Zakaluk and Sri Ranjan 2006;Byrd et al 2014) water potential at noon (or close to this time). However, under humid environments with low VPD, maximum stomatal closure during the day depends on radiation increments and VPD (Table 2).…”

Section: Discussionmentioning

confidence: 99%

Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions

et al. 2018

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Low radiation is one of the most important factors which limits potential yield in potato. Under humid conditions, the dominance of diffuse radiation not only imposes challenges for radiation use efficiency in crops but also limits the water status surveillance through non-invasive methods like infrared radiometry. This study was carried out in the humid desert of the Peruvian central coast with the aim to relate maximum stomatal conductance (g s_max , an important water status indicator) with leaf and air temperature (dT) and crop water stress index (CWSI). In a potted trial, g s_max vs. dT were compared along the day in well-irrigated (field capacity) and water restricted (half field capacity) plants. In an additional field experiment, CWSI was validated by testing two irrigation timing treatments with pre-established g s_max threshold (0.15 [T1] and 0.50 [T2] mol H 2 O m −2 s −1) against a control (frequently irrigated). An acute stomatal closure sensitivity was detected which drove a g s_max fall (g s ↓) near the solar noon. The intense stomatal closure caused a dT rise which showed positive higher values (> 2°C) after g s ↓. The significant yield reduction of T1 in relation to the control (− 38.2 ± 10.7%) highlighted that g s_max values > 0.15 must be used to warrant a high potato yield. These findings support the use of CWSI values ≤ 0.3-0.4 as thresholds for an appropriate irrigation in potatoes with assessments taken at around 15:00 hours, time in which plants have accumulated enough radiation allowing an appropriate detection of thermal emission under humid conditions.

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Section: Discussionmentioning

confidence: 99%

Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions

et al. 2018

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“…Specifically, the color and texture features of images were extracted and then used as inputs to the neural network to detect disease [15]. Zakaluk and Sri Ranjan used digital camera-acquired tomato images under four soil water stress levels and built an artificial neural network model with RGB images to determine the leaf water potential [16]. Support vector machine (SVM) and the Gaussian processes classifier (GPC) were applied to automatically detect regions in spinach canopies with different soil moisture levels based on thermal and digital images [17].…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

Mao-song

et al. 2019

Symmetry

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Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.

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“…The main features of the color parameters based on the works of Leon et al (2006) and Zakaluk and Ranjan (2006) are detailed in Table 1, which shows that each color space was developed for a particular purpose; as a result, each color space has certain advantages when used in classification and identification problems.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study

Cited by 56 publications

References 35 publications

Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions

Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions

Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

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