Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing

Kumar, Ajay; Shreeshan, S; Tejasri, N; Rajalakshmi, P.; Guo, Wei; Naik, B. Balaji; Marathi, Balram; Desai, U.B.

doi:10.1109/ingarss48198.2020.9358930

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…(2018) offers a thorough evaluation of DL methods applied to a broad spectrum of plant species, focusing on tasks such as identifying, classifying, quantifying, and predicting plant stress. The other studies of Kumar et al. (2020) ; Tejasri et al.…”

Section: Introductionmentioning

confidence: 94%

“…The exhaustive review from Singh et al (2018) offers a thorough evaluation of DL methods applied to a broad spectrum of plant species, focusing on tasks such as identifying, classifying, quantifying, and predicting plant stress. The other studies of Kumar et al (2020); Tejasri et al (2022) explored UAV-captured imagery for predicting water stressaffected crops using CNN-based frameworks. These studies highlight that Red, Green, and Blue (RGB) bands are crucial for classifying water-stressed crops due to their rich properties of colour and texture.…”

Section: Introductionmentioning

confidence: 99%

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StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

Nampally,

Kumar,

Chatterjee

et al. 2023

Front. Plant Sci.

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In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and Bi-Directional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatial-spectral-temporal characteristics for water stress classification.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

Nampally,

Kumar,

Chatterjee

et al. 2023

Front. Plant Sci.

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“…Fuentes et al used ResNet50 as the feature extractor in the SSD target detection framework to identify potato diseases, resulting in an accuracy rate of 85.98%. Similarly, Kumar et al (Kumar et al, 2020) implemented the ResNet34 model to identify 14 different crop diseases on the Plant Village dataset, with a high accuracy rate of 99.40%.…”

Section: Convolutional Neural Network Modelsmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…Therefore, ensuring the agricultural sectors are productive and sustainable requires better water management in agriculture. A convolutional neural network model was presented in article [6] to identify the waterstressed and typical locations in the maize crop field. A comparison of the suggested framework's performance against ResNet50, VGG-19, and Inception-v3 reveals that, with an accuracy of 93%, the suggested model produced superior results.…”

Section: Determining the Water Stressmentioning

confidence: 99%

“…Plant illnesses can be detected and prevented early with the use of AI, which means that less treatments would be needed to stop their spread, which would drastically lower environmental contamination [5]. For plants to be healthy, grow, and yield, agronomic inputs like water, nutrients, and fertilisers must be continuously available [6]. Both biotic and abiotic stress may result from the lack of any of these sources.…”

Section: Introductionmentioning

confidence: 99%

Application of Robotics, Artificial Intelligence and Deep Learning in Modern Agriculture Technology: A Review

Prajapati,

Kadam,

Aitwar

et al. 2023

IJPSS

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In order to determine their potential impact in the field of agriculture, the proposed work aims to review the various artificial intelligence (AI) techniques, with a focus on expert systems, robots designed specifically for agriculture, and sensors technology for data collection and transmission. These techniques include fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimisation (PSO), artificial potential field (APF), simulated annealing (SA), deep learning. The application of AI techniques and robots in cultivation, monitoring, and harvesting is not highlighted in any literature, making it difficult to compare each one simultaneously based on popularity and usefulness while also understanding how each contributes to the agricultural industry. With knowledge of the extent of AI engaged and the robots used, this paper compares three crucial agricultural phases: cultivation, monitoring, and harvesting. The current study offers a comprehensive analysis of over 200 publications that cover the use of automation in agriculture as of 1960 and 2021. It draws attention to the unmet research needs for developing intelligent, self-governing agricultural systems. The frequency of various AI, robotics and deep learning techniques for particular applications in the agriculture industry round out the article.

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Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing

Cited by 12 publications

References 10 publications

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

Application of Robotics, Artificial Intelligence and Deep Learning in Modern Agriculture Technology: A Review

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