Deep Learning Methods and Applications for Precision Agriculture

Ganatra, Nilay; Patel, Atul

doi:10.1007/978-981-15-7106-0_51

Cited by 15 publications

(2 citation statements)

References 13 publications

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“…In recent years, studies have reported on the introduction of DL methods into the field of remote sensing image processing [16], [17]. In the field of agricultural remote sensing, DL methods are also being used more and more widely [18], and are mainly applied to crop classification [19], crop growth dynamic monitoring [20], [21] and crop disease monitoring [22], [23]. Crop dynamics monitoring requires high timeliness of remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Combining Sentinel-1 and -3 Imagery for Retrievals of Regional Multitemporal Biophysical Parameters Under a Deep Learning Framework

Han

Wang

Tansey

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Regions with excessive cloud cover lead to limited feasibility of applying optical images to monitor crop growth. In this study, we built an upsampling moving window network for regional crop growth monitoring (UMRCGM) model to estimate the two key biophysical parameters (BPs), leaf area index (LAI) and canopy chlorophyll content (CCC) during the main growth period of winter wheat by using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-3 optical images. Sentinel-1 imagery is unaffected by cloudy weather and Sentinel-3 imagery has a wide width and short revisit period, the organic combination of the two will greatly improve the ability to monitor crop growth at a regional scale. The impact of two different types of SAR information (intensity and polarization) on the estimation of the two BPs was further analyzed. The UMRCGM model optimized the correspondence between inputs and outputs, it had more accurate LAI and CCC estimates compared with the three classical machine learning models, and had the highest accuracy at the green-up stage of winter wheat, followed by the jointing stage and the heading-filling stage, and the lowest accuracy was found at the milk maturity stage. The estimation accuracies of CCC were slightly higher than that of LAI for the first three growth stages of winter wheat, while lower than that of LAI for the milk maturity stage. This study proposes a new method for regional BPs (especially for CCC) estimation by combining SAR and optical imagery with large differences in spatial resolution under a deep learning framework.

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Section: Introductionmentioning

confidence: 99%

Combining Sentinel-1 and -3 Imagery for Retrievals of Regional Multitemporal Biophysical Parameters Under a Deep Learning Framework

Han

Wang

Tansey

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Diverse fields of applications are increasingly employing deep learning due to its capacity for feature learning. The deep learning algorithm will utilize the composition of the lower-level features to yield the hierarchy of the higher-level features, and thereby, accomplish the automatic extraction of features from the raw dataset which has been provided (Ganatra and Patel, (2021).…”

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confidence: 99%

Identifying pests in precision agriculture using low-cost image data acquisition

Hemalatha,

Sangeetha

2024

Braz. J. Biol.

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Unmanned Aerial Vehicles (UAVs), often called drones, have gained progressive prevalence for their swift operational ability as well as their extensive applicability in diverse real-world situations. Of late, UAV usage in precision agriculture has attracted much interest from scientific community. This study will look at drone aid in precise farming. Big data has the ability to analyze enormous amounts of data. Due to this, it is one of the diverse crucial technologies of Information and Communication Technology (ICT) which had applied in precision agriculture for the abstraction of critical information as well as for assisting agricultural practitioners in the comprehension of the most feasible farming practices, and also for better decision-making. This work analyses communication protocols, as well as their application toward the challenge of commanding a drone fleet for protecting crops from infestations of parasites. For computer-vision tasks as well as data-intensive applications, the method of deep learning has shown much potential. Due to its vast potential, it can also be used in the field of agriculture. This research will employ several schemes to assess the efficacy of models includes Visual Geometry Group (VGG-16), the Convolutional Neural Network (CNN) as well as the Fully-Convolutional Network (FCN) in plant disease detection. The methods of Artificial Immune Systems (AIS) can be used in order to adapt deep neural networks to the immediate situation. Simulated outcomes demonstrate that the proposed method is providing superior performance over various other technologically-advanced methods.

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