Agriculture Field Characterization Using Aerial Photograph and Satellite Imagery

Basnyat, Prakash; McConkey, B.G.; Meinert, B.; Gatkze, C.; Noble, Gordon

doi:10.1109/lgrs.2003.822313

Cited by 20 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there are other vegetation indices with global utility, many of which incorporate a soil adjustment factor, NDVI was used due to its simplicity and proven usefulness in estimating vegetation properties relevant to crop production (Basnyat et al 2004a). …”

Section: Satellite Remote Sensed Informationmentioning

confidence: 99%

“…1) that fell into that zone. These means were compared by least significant difference test at P = 0.05 from analysis of variance using SYSTAT 10 (SPSS 2000) as described by Basnyat et al (2004a). If all delineated zones had different grain yields, then the number of zones to delineate would be increased by one and the "Fuzme" algorithm re-run.…”

Section: Delineation Of Zonesmentioning

confidence: 99%

See 1 more Smart Citation

Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics

Basnyat¹,

McConkey²,

Selles³

et al. 2005

Can. J. Soil. Sci.

View full text Add to dashboard Cite

Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics. Can. J. Soil Sci. 85: 319-328. Cost-effective methods to map differences in productivity across fields have potential application for site-specific management of fertilizer and pesticides. In this study, zones were delineated for a field with hummocky topography in southwestern Saskatchewan by clustering the normalized difference vegetation index (NDVI) derived from Landsat TM information. Zones uniqueness were confirmed if zones differed in grain yield. Two different zones were delineated in the field. These zones had significant (P < 0.05) differences in soil factors related to productivity: average solum depth, spring soil nitrogen (N), phosphorus (P), and spring soil moisture. Over 4 yr, the two zones also had different spring wheat (Triticum aestivum L.) grain yield and protein content. The yield response to N-P fertilizer blend within each zone was different with no significant response to N-P fertilizer in the zone with higher NDVI values versus a significant response to N-P in the zone with lower NDVI. The results indicate a potential economic advantage to reducing fertilizer application to the zone without fertilizer response. Further, the residual soil NO 3 -N in the zone without fertilizer response was positively correlated with N application in the previous year. Therefore, there is a potential environmental benefit to reducing fertilizer application to that zone to decrease residual NO 3 -N, which can leach and contaminate ground water or can be denitrified to the greenhouse gas, N 2 O. Hence, this relatively simple and low-cost method of zone delineation has potential practical application to realize economic and environmental benefits from site-specific management of fertilizer. Un rendement grainier différent a servi à confirmer le caractère unique de ces zones. Deux zones distinctes ont été définies sur le terrain. Ces zones se distinguaient par une variation significative (P < 0,05) des facteurs pédologiques associés à la productivité : l'épaisseur moyenne des horizons, la concentration d'azote (N) dans le sol au printemps, celle de phosphore (P) et la teneur en eau à la même époque. En quatre ans, les deux zones ont aussi donné un rendement grainier et une teneur en protéines différents pour le blé de printemps (Triticum aestivum L.). La réaction du rendement au mélange d'engrais N et P différait dans chaque zone, sans réaction significative dans celle à l'indice de végétation le plus élevé et avec une réaction significative dans celle où l'indice était plus faible. Les résultats indiquent qu'une diminution de la quantité d'engrais appliquée dans la zone ne réagissant pas aux amendements pourrait présenter un certain potentiel économique. Par ailleurs, la quantité de N-NO 3 résiduelle dans la zone ne réagissant pas aux amendements est positivement corrélée à l'application de N l'année antérieure. Une réduction des applications d'engrais dans cette zone pourrait donc avoir des...

show abstract

Section: Satellite Remote Sensed Informationmentioning

confidence: 99%

Section: Delineation Of Zonesmentioning

confidence: 99%

Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics

Basnyat¹,

McConkey²,

Selles³

et al. 2005

Can. J. Soil. Sci.

View full text Add to dashboard Cite

show abstract

“…Different types of beers are classified by color information [5]. In [6], infrared imaging is used to characterize the agriculture fields. Image processing has been extended to diagnose disease of the plant [7,8].…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Growth of Green Bean Sprouts with Linear Discriminant Analysis

Kim

Yeom

2017

IJFIS

View full text Add to dashboard Cite

Machine vision technique is playing an increasingly important role in monitoring plant growth. The identification of good or bad status of growing plant requires specific features such as color, length, and solidity of stems. This paper addresses recognition of growing status of the green bean sprout. For preprocessing, RGB color images are converted to hue-saturationintensity (HSI) images, and then, principal component analysis (PCA) is applied to reduce the high dimensionality of image data. Finally, linear discriminant analysis (LDA) recognizes whether the growing status is good or bad. In the experiments, the images of the green bean sprouts growing in jars are acquired for 6 consecutive days. The correct classification rates are compared between PCA only process and LDA combined with PCA. The experimental results show PCA+LDA can achieve more than 93.3% average accuracy after 3 days.

show abstract

“…However, the area typically has considerable differences in its characteristics, especially in regard to soil type, due to pedogenetic processes and factors of soil formation (SOUZA et al, 2008), which leads to variable productivity. For this reason, the identification of management zones that take into account the distribution of soil attributes is important to redefine management practices to minimize costs and maximize yield (BASNYAT et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Prediction of soil chemical attributes using optical remote sensing

Genú

Demattê

2011

Acta Sci. Agron.

View full text Add to dashboard Cite

ABSTRACT. Soil fertility variability management is one of the pioneering and important areas in which Precision Agriculture has been commercially applied. Consequently, the objective of this work was to predict soil chemical attributes through spectral responses. The 1,000 ha study area used for this report was located in Uberlândia, Minas Gerais State, Brazil. Thirty sampling points were established, at which the soil was collected at 3 different depths. The samples were chemically and physically analyzed and the radiometric data obtained in the 400 -2500 nm range. Multiple regression equations were generated for sum of bases, cation exchange capacity, base saturation, aluminum saturation, pH, P, K, Ca, Mg, Al, and H, all using 60 soil samples. H, Al, m%, and pH were found to have R 2 values less than 0.50. Equations with an R 2 > 0.50 for the other attributes were tested for the 30 unknown soil samples, and the estimated values were obtained. These values were then compared with those determined by conventional analysis. The coefficients of correlation were higher than 50% for all attributes except P and V%. Results indicated that determining chemical attributes with models that are specific for the region is feasible.Keywords: soil chemistry, reflectance, laboratory sensor. RESUMO. Predição de atributos químicos do solo utilizando sensoriamento remoto ótico. Uma das primeiras e mais importantes áreas nas quais a Agricultura dePrecisão está sendo comercialmente aplicada é o manejo da variabilidade da fertilidade do solo. Desta forma, o objetivo deste trabalho foi predizer o teor dos atributos químicos do solo através da sua resposta espectral. A área de estudo de 1000 ha localiza-se em Uberlândia, Estado de Minas Gerais. Estabeleceu-se 30 pontos de amostragem nos quais o solo foi coletado em 3 profundidades. As amostras foram analisadas química e fisicamente e, os dados radiométricos obtidos com um sensor em laboratório na faixa de 400 -2500 nm. Equações de regressão múltipla foram geradas para soma de bases, CTC, saturação por bases, saturação por alumínio, pH, P, K, Ca, Mg, Al e H usando 60 amostras de solo. H, Al, m% e pH obtiveram R 2 < 0,50. As equações com R 2 > 0,50 para os outros atributos foram testadas para as 30 amostras de solo desconhecidas obtendo-se os valores estimados. Posteriormente, estes valores foram comparados com aqueles determinados pela análise convencional. Os coeficientes de correlação foram maiores que 50% para todos os atributos exceto P e V%. Os resultados indicam que é possível determinar atributos químicos com modelos específicos para a região.Palavras-chave: química do solo, reflectância, sensor em laboratório.

show abstract

Agriculture Field Characterization Using Aerial Photograph and Satellite Imagery

Cited by 20 publications

References 11 publications

Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics

Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics

Investigation on the Growth of Green Bean Sprouts with Linear Discriminant Analysis

Prediction of soil chemical attributes using optical remote sensing

Contact Info

Product

Resources

About