Utilization of Machine Vision to Monitor the Dynamic Responses of Rice Leaf Morphology and Colour to Nitrogen, Phosphorus, and Potassium Deficiencies

Sun, Yuanyuan; Gao, Jiali; Wang, Ke; Shen, Zhangquan; Chen, Lisu

doi:10.1155/2018/1469314

“…The performance of the validation sets was considerably lower than that of the training sets, but the accuracy at distinguishing between various levels of P treatment were equal or higher than 80% in 11 variants among 15 variants of species/stages of plant development. This result is very good although difficult to compare with other studies that used different experimental setups and limited numbers of P treatment variants [ 38 , 42 ]. The lowest percentages of correctly classified instances were obtained for the first stage of plant development; however, with progress in the development of plants, this accuracy was higher.…”

Section: Resultsmentioning

confidence: 70%

“…Most of these studies have focused on the direct prediction of P content based on reflectance indices combining a few spectral bands or on indirect detection by predicting the content of a related substance (e.g., chlorophyll content). Until now, few investigations have been dedicated to analysing the temporal dynamics of leaf morphology and colour under different P treatments covering longer periods of plant growth and development and multiple bands of visible/infrared spectrum [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Siedliska

¹

,

Baranowski

²

,

Pastuszka-Woźniak

³

et al. 2021

View full text Add to dashboard Cite

Background Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

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“…The performance of the validation sets was considerably lower than that of the training sets, but the accuracy at distinguishing between various levels of P treatment were equal or higher than 80% in 11 variants among 15 variants of species/stages of plant development. This result is very good although di cult to compare with other studies that used different experimental setups and limited numbers of P treatment variants [38,42]. The lowest percentages of correctly classi ed instances were obtained for the rst stage of plant development;…”

Section: E Results Of Discrimination Analysismentioning

confidence: 66%

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Siedliska¹,

Baranowski²,

Pastuszka-Woźniak³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

show abstract

“…The performance of the validation sets was considerably lower than that of the training sets, but the accuracy at distinguishing between various levels of P treatment were equal or higher than 80% in 11 variants among 15 variants of species/stages of plant development. This result is very good although di cult to compare with other studies that used different experimental setups and limited numbers of P treatment variants [34,38]. The lowest percentages of correctly classi ed instances were obtained for the rst stage of plant development; however, with progress in the development of plants, this accuracy was higher.…”

Section: Effective Wavelength Selectionmentioning

confidence: 59%

“…Most of these studies have focused on the direct prediction of P content based on re ectance indices combining a few spectral bands or on indirect detection by predicting the content of a related substance (e.g., chlorophyll content). Until now, few investigations have been dedicated to analysing the temporal dynamics of leaf morphology and colour under different P treatments covering longer periods of plant growth and development and multiple bands of visible/infrared spectrum [37,38].…”

Section: Introductionmentioning

confidence: 99%

Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

Siedliska¹,

Baranowski²,

Pastuszka-Woźniak³

et al. 2020

Preprint

2

0

View full text Add to dashboard Cite

Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

show abstract

Utilization of Machine Vision to Monitor the Dynamic Responses of Rice Leaf Morphology and Colour to Nitrogen, Phosphorus, and Potassium Deficiencies

Cited by 15 publications

References 21 publications

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

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