Remote Sensing: An Advanced Technique for Crop Condition Assessment

Ennouri, Karim; Kallel, A.

doi:10.1155/2019/9404565

Cited by 37 publications

(18 citation statements)

References 57 publications

(60 reference statements)

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“…Remote sensing combined with an unsupervised classification algorithm allows, at the appropriate time, monitoring and mapping of attacks by T. peregrinus in eucalyptus plantations (Ennouri and Kallel 2019).…”

Section: Discussionmentioning

confidence: 99%

Monitoring Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae) in Eucalyptus plantations with remote sensing

Santos¹,

Costa

Rosa

et al. 2020

BJD

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Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae), the eucalyptus bronze bug, is among the many species of insect pests that affect commercial eucalyptus forests in Brazil. T. peregrinus reduces the photosynthetic capacity of trees and, in some cases, can lead to the complete death of plants. The objective of this research was to evaluate the potential of medium spatial resolution images, available for free, in the mapping and prediction of attacks caused by T. peregrinus in eucalyptus plantations in Brazil, using Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA regression model selected three main components, with a cross-validation error rate of 0.245 for the prediction and mapping of stands attacked by T. peregrinus. The important bands were selected from the PLS-DA model, using variable importance in the projection (VIP). The VIP bands predicted healthy and Palavras-chave: Análise discriminante dos mínimos quadrados parciais; Análise multivariada; Insetos-praga; Imagens de média resolução.

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Section: Discussionmentioning

confidence: 99%

Monitoring Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae) in Eucalyptus plantations with remote sensing

Santos¹,

Costa

Rosa

et al. 2020

BJD

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“…Satellite imagery refers to the assignment of globe imagery from detectors and sensors placed on sophisticated satellites in orbit around the globe [6,21]. Satellite images provide significant data that can be used in a number of remote sensing applications, such as meteorology, cartography, urban change recognition, and agricultural inspection.…”

Section: Satellite Imagerymentioning

confidence: 99%

“…In addition, support vector machines are a kind of machine learning classifier, possibly one of the most common types of classifiers. Support vector machines are particularly useful for numerical prediction, categorization, and sample detection tasks [12,21]. Support vector machines run through the representation of decision limits between records points, aiming at a decision boundary that most excellently divides the information points into categories.…”

Section: Change Detectionmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

Self Cite

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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“…The methods on sugarcane plant growth and yield estimation has been analyzed in this part. A satellite image based sugarcane crop yield estimation is presented in (3) , which consider different features and applies image processing methods towards crop yield estimation. A mathematical model is presented towards crop yield estimation which consider different features being extracted from satellite images and uses remote sensing approaches.…”

Section: Introductionmentioning

confidence: 99%

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks

Saranya¹,

Thenmozhi²

2020

IJST

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Objective: To improve the performance of an efficient satellite image based CCF (Color, Climate, Flow) approximation model is presented in this article. Method: We attempted for plant growth estimation and yield estimation using artificial neural networks. The model receives the satellite images and preprocesses to improve the quality of the image. From the quality improved image, the method extracts the color values. Further, the features like climate and flow features from the data set of the region have been extracted. Using these features, for different time window, the method generates number of neurons and initializes them with the set of features. The set of images from the data set are used to extract several features and used to train the network. The classification is performed according to the same set of features obtained from input satellite image and the other features of the region at current window. The method estimates CCF plant growth and yield support measures as result. Based on these values, the yield estimation is performed. Result: The proposed method improves the performance of plant growth up to 97.25 and the yield estimation performance is increased up to 985 which is higher than previous approaches. Provide comparative estimation. Novelty: The proposed CCF model consider color, climate and features in plant growth estimation which differ from other approaches by considering maximum features obtained from satellite images as well as features collected from ground truth in different time stamp.

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Remote Sensing: An Advanced Technique for Crop Condition Assessment

Cited by 37 publications

References 57 publications

Monitoring Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae) in Eucalyptus plantations with remote sensing

Monitoring Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae) in Eucalyptus plantations with remote sensing

Detection of Urban and Environmental Changes via Remote Sensing

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks

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