Identifying crop water stress using deep learning models

Chandel, Narendra Singh; Chakraborty, Subhasish; Rajwade, Yogesh Anand; Dubey, Kumkum; Tiwari, Mukesh; Jat, Dilip

doi:10.1007/s00521-020-05325-4

Cited by 74 publications

(37 citation statements)

References 45 publications

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“…Although sorghum crops are tolerant to drought stress, the quantity and quality of the grain yield can adversely be affected when imposed to water stress [1]. The tractability of biotic and abiotic stress on phenotypic characteristics of crops in the field through computer vision, machine learning, and image processing techniques have been demonstrated in several reports [35][36][37]. Crops display several mechanisms including morphological, physiological, and biochemical mechanisms to mitigate the effect of water stress resulting in different phenotypes that can be differentiated using the feature extraction method [38].…”

Section: Resultsmentioning

confidence: 99%

The effect of drought stress of sorghum grains on the textural features evaluated using machine learning

Ropelewska

Nazari

2021

Eur Food Res Technol

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This study aimed to determine the discriminatory power of textural features to differentiate the sorghum grains subjected to normal, mild deficit, and severe deficit irrigation. The studies were carried out with the use of image processing, discrimination analysis, analysis of variance and cluster analysis using the selected texture parameters calculate for images from individual color channels L, a, b, R, G, B, U, V, S, X, Y and Z. The results indicated that different levels of irrigation can discriminate the sorghum grain with an accuracy of up to about 100%. Most of the genotypes for each level of irrigation were different in the terms of values of textural features and formed separate homogeneous groups. Drought is one of the limiting factors contributing to a decrease in sorghum grain productivity and nutritional quality, especially when it is cultivated in a marginal area. Therefore, low-quality grains produced under water stress should be recognized before they enter into the food and feed chain. The application of image analysis based on textures of sorghum grain images proved to be useful for the discrimination of sorghum grains subjected to drought stress. The applied procedure provided the fast, objective results that may be applied in practice for screening distinguishing the sorghum grains with different irrigation levels.

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Section: Resultsmentioning

confidence: 99%

The effect of drought stress of sorghum grains on the textural features evaluated using machine learning

Ropelewska

Nazari

2021

Eur Food Res Technol

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“…Machine learning is already used in agriculture for many purposes, including soil mapping, water management, crop yield prediction, carcass weight prediction, and image processing to detect water stress, pests, and disease. 180,181,182,183,184,185 However, the widespread adoption of "precision agriculture" and "smart farm technologies" has varied with technology and other factors. 186,187 Similarly, machine learning is already used in wastewater treatment to monitor influent conditions, optimize maintenance and treatment parameters, and predict effluent concentrations.…”

Section: Challengesmentioning

confidence: 99%

National Alliance for Water Innovation (NAWI) Agriculture Sector Technology Roadmap 2021

Dionysiou¹,

Katz²,

Xu³

et al. 2021

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Cost metrics can include levelized costs of water treatment as well as individual cost components, such capital or operations and maintenance (O&M) costs. Energy PerformanceEnergy performance metrics can include the total energy requirements of the water treatment process, the type of energy required (e.g., thermal vs. electricity), embedded energy in chemicals and materials, and the degree to which alternative energy resources are utilized. Water Treatment PerformanceWater treatment performance metrics can include the percent removal of various contaminants of concern and the percent recovery of water from the treatment train. Human Health and Environment ExternalitiesExternality metrics can include air emissions, greenhouse gas emissions, waste streams, societal and health impacts, land-use impacts. Process AdaptabilityProcess adaptability metrics can include the ability to incorporate variable input water qualities, the ability to incorporate variable input water quantity flows, the ability to produce variable output water quality, and the ability to operate flexibly in response to variable energy inputs.

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“…Chandel et al [113] evaluated instead the three different popular CNN models of AlexNet, GoogLeNet and Inception V3 for plant water stress classification of maize, okra and soybean images collected from different growth stages. The performance of GoogLeNet was found to be superior compared to others with an accuracy of 98.3%, 97.5% and 94.1% for maize, okra and soybean, respectively.…”

Section: Plant Water Stress Identificationmentioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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Identifying crop water stress using deep learning models

Cited by 74 publications

References 45 publications

The effect of drought stress of sorghum grains on the textural features evaluated using machine learning

The effect of drought stress of sorghum grains on the textural features evaluated using machine learning

National Alliance for Water Innovation (NAWI) Agriculture Sector Technology Roadmap 2021

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

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