Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis

Farg, Eslam; Nabil, Mohsen; Arafat, S.M. Yasir

doi:10.1016/j.ejrs.2019.07.002

Cited by 8 publications

(10 citation statements)

References 12 publications

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“…Visual or multispectral sensors are the most common form of sensor available across satellites. For example, prior works [15][16][17][18] have used Landsat satellite data in the visible and near IR region, but without explicitly stating the sensor type. This might have influenced not only the findings according to sensor type, but have also resulted in fewer number of studies when the search was limited to sensor type rather than platform.…”

Section: Temporal Trendsmentioning

confidence: 99%

Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities

et al. 2020

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Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability.

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Section: Temporal Trendsmentioning

confidence: 99%

Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities

et al. 2020

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“…The procedure of the adopted scheme is as follows: In the conventional MSA scheme, the step length is determined as per Eq. (16), where the parameters d and v were chosen in a random manner. As per the proposed model, the parameters d and v in Eq.…”

Section: 3proposed Algorithmmentioning

confidence: 99%

“…Therefore, there raised a requirement to categorize the crops for an efficient geographical study. Globally, the most important classifications of crops [15] [16] [17] are portrayed by the FAO of UN. Therefore, more awareness is needed regarding the classification of crops and plants for better knowledge of their distribution across the globe and in India [18] [19] [20].…”

Section: Introductionmentioning

confidence: 99%

Meta-Heuristic based Trained Deep Convolutional Neural Network for Crop/Weed Classification

Gomathi¹

2020

IJETER

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Computer vision and camera sensors are hopeful technologies for capturing information and processing to facilitate autonomous cultivation with machine learning. Nowadays, field robots are widely utilized, which autonomously navigates in fields tasks for advanced developments. However, manual activities are also required for certain needs, for instance, controlling weed in organic carrot farming is carried out manually and it is essential to evade considerable crop yield loss. This paper makes an attempt to introduce a new automatic crop/weed classification under three major phases: (i) Pre-processing (ii) Feature Extraction and (iii) Classification. Initially, the images are converted to greyscale images under pre-processing stage. Further, from the pre-processed image, the features like "Grey Level Co-occurrence Matrix (GLCM) and Grey Level Runlength Matrix (GLRM) based texture features" are extracted. Finally, the classification is done by the hybrid classifiers, where both the Deep Convolutional Neural Network (DCNN) and Neural Network (NN) is incorporated. Finally, both the classified outputs are ORed to get the final classification output. Moreover, in order to enhance the performance of proposed work, it is planned to tune the hidden neurons of NN optimally via a new improved Moth Search Algorithm (MSA) and is named as Moth Search with newStep Length evaluation (MS-SL). Finally, the performance of proposed work is evaluated over other state-of-the-art models with respect to certain performance measures.

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“…Comparative accuracy analysis showed that using time series S2 spectral bands (SBs) and S1 polarization channels with some added S1 textural features could use as best integration scenario that achieved the highest accuracy (OA = 96.31 & Kappa = 0.96) was adding S1 textural features to S2 spectral features. In addition to [ 10 ], used time series of satellite images calculated Vegetation Index (VI)'s the results showed that the best representation of the crop phenological changes during the crop growth season and higher accuracy in strategic crops discrimination with overall kappa accuracy with 0.82 and 0.79 respectively. On the other hand, shortage of processing power and huge amount of data were a challenge for proposed method to apply on all Egypt.…”

Section: Introductionmentioning

confidence: 99%