Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Osco, Lucas Prado; Ramos, Ana Paula Marques; Moriya, Érika Akemi Saito; Bavaresco, Lorrayne Guimarães; Lima, Bruna Coelho de; Estrabis, Nayara Vasconcelos; Pereira, Danillo Roberto; Creste, José Eduardo; Marcato, José; Gonçalves, Wesley Nunes; Imai, Nilton Nobuhiro; Li, Jonathan; Liesenberg, Veraldo; Araújo, Fábio Fernando de

doi:10.3390/rs11232797

Cited by 36 publications

(25 citation statements)

References 33 publications

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“…Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1][2][3][4], the stress response [5][6][7], plant count [8,9], yield prediction [10][11][12], chlorophyll content [13][14][15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19][20][21][22][23]. This type of sensor can measure the spectral response of a target at very-high resolutions while having a reductive amount of radiometric interference by being near the leaf sample.…”

Section: Introductionmentioning

confidence: 99%

“…They also have the advantage of helping to define, in detail, the appropriate spectral regions to estimate these phenomena. This definition is relatively important as it can guide future research towards the development of equipment specifically designed to measure these regions [23]. Another type of contribution is that it can assist in creating spectral vegetation indices or other simpler mathematical models that contribute to identifying the different characteristics of plants [13,28].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

et al. 2020

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Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

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“…Although spectral indices have relatively significant relationships with physiological measurements, the use of a single vegetation index for predicting crop water status may not be optimal. Innovative approaches based on machine learning and multiple spectral dimensions could be beneficial [38].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike space-borne satellites, handheld spectroradiometer and unmanned aerial vehicle (UAV) allows for local mapping at higher spatial and temporal resolutions. Moreover, they can monitor plant water status and aid irrigation water management [30,37,38]. Due to handheld spectroradiometer's limited range and UAVs' limited flight time, they require more measurements to cover a large area [25,39].…”

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Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

et al. 2020

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Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R 2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R 2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.have devastating impacts on the environment [3]. For agriculture, drought can endanger food security which can have critical effects on the economy, geopolitics, and society [5]. Drought can also negatively affect agriculture production especially in arid and semiarid regions [6]. Therefore, detecting crop water stress is of vital importance to improve agricultural quality and productivity.Cotton is cultivated in arid and semiarid regions and is the most important fiber crop in the world [7]. Cotton is susceptible to water stress in all growth stages, particularly during the flowering and boll development stages [8]. Moreover, water deficit is an important factor limiting growth and development in cotton, including plant height, leaf and root size, as well as economic yield and fiber quality [8]. To better understand the cotton's response and adaption to water stress, continuous monitoring of cotton water status in large a...

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Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Cited by 36 publications

References 33 publications

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

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