Deep learning for identification of water deficits in sugarcane based on thermal images

Melo, Leonardo Leite de; Melo, Verônica Gaspar Martins Leite de; Marques, Patrícia Angélica Alves; Frizzone, José Antônio; Coelho, Rubens Duarte; Romero, Roseli Aparecida Francelin; Barros, Timóteo Herculino da Silva

doi:10.1016/j.agwat.2022.107820

Cited by 12 publications

(4 citation statements)

References 45 publications

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“…This is the reason for RGB images also yielding satisfactory accuracy of up to 94.6% for tracing leaf color changes [ 63 ]. Compared to RGB imagery, thermal imagery is a more detailed and better presenter of the crop stress that alters the emissivity patterns proportionally [ 64 , 65 ]. The canopy temperature is affected by the microclimate conditions and the available soil moisture [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…Color and grey images of maize were also used as inputs to the DCNN model for water stress identification where stress identification and classification accuracies were 98% and 95%, respectively [ 26 ]. The inception-ResNet V2 framework utilized for water stress identification in sugarcane yielded an accuracy of 83% with available soil water capacity as input [ 65 ]. Thus far, most of the computer vision models have utilized single-dimensional data inputs, unlike this study which advances water-stress identification in wheat crops using multidimensional data inputs.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…of time duration to observe with 83% of high precision. [283] Thanks to nanobiotechnology, [284] owed to which agricultural nanobionics could be implement to supplement a rising number of devices for swift and phenotyping of large plants to research complicated signaling channels related to plants health and the probes readout can be interconnected with versatile, cost effective electronics, conceivably broadening nanobionic execution outwith plant biological research for encouraging onground plants real-time health monitoring. [285] Acquisitioning smart autonomous remote sensing and self-management (Figure 25b) requisite infrared-based nanosensors integration for plants early stress identification and designing smart agricultural monitoring tools that converts changes in stress sensing to optical waves, particularly near infrared waves, which are collected and decoded by agricultural farming machinery, facilitating plants health by interaction among nanomaterials-engineered plants and agricul-tural facilities.…”

Section: Agricultural Protectionmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

Small

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Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro‐scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.

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“…Similarly, Normalized Difference Red-Edge (NDRE) [ 12 ] and Red-edge Chlorophyll Index (RCI) [ 13 , 14 ] are used to predict chlorophyll concentration in plant tissues [ 15 ]. Multispectral imaging processing and machine learning techniques have also been used to detect abiotic and biotic stresses in plants, such as root water stress [ 16 , 17 ], and tomato spotted wilt virus and powdery mildew [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

Sharma

Banerjee

Hayden

et al. 2023

Plants

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Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.

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Deep learning for identification of water deficits in sugarcane based on thermal images

Cited by 12 publications

References 45 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

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

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