Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks

Kapp, Claudio; Caires, Eduardo Fávero; Guimarães, Alaine Margarete

doi:10.4018/ijaeis.2014040103

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Based on the regression equation for grain yield (Fig. 2b) Since the soil N availability is dependent on mineralization of organic matter [5] and due to the essential role of N in high-yield systems [7], positive effects of N fertilization on wheat yields have been frequently observed in studies conducted on Brazilian soils [11,17,28]. In our study conducted on an Oxisol with 35 g dm −3 of organic matter in the topsoil (Table 1), a rate of 55 kg N ha −1 for wheat cultivar Quartzo following soybean provided a maximum economic return to obtain grain yield of 4250 kg ha −1 .…”

Section: Effect Of N Fertilization On Wheat Shoot Dry Matter and Graimentioning

confidence: 99%

“…3). NDVI variations depending on the level of N fertilization are usually fitted by linear or quadratic equations [1,11]. Although wheat shoot dry matter and grain yields at physiological maturation were not affected by the level of N fertilization at sowing, the NDVI values showed that N fertilization at sowing favored the early growth of wheat plants.…”

Section: Effect Of Sensor Height On Wheat Spectral Reflectance Measurmentioning

confidence: 99%

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Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

2020

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A field experiment examined the wheat response following soybean due to urea-N fertilization at sowing (0, 20, 40, and 60 kg N ha −1) and top dressing (0, 30, 60, and 90 kg N ha −1). Normalized difference vegetation index (NDVI) measurements using a GreenSeeker active sensor were taken at different growth stages, at various sensor heights above the canopy, and at different times of day. Both the shoot dry matter yield and the grain yield were not affected by N fertilization at sowing and increased with increasing N rates at top dressing. A maximum economic yield was obtained at 55 kg N ha −1 in top dressing, causing a 33% increase in grain yield and an economic return of US$ 189.50 ha −1. Differences in NDVI were found before and after top-dressing N fertilization, indicating that GreenSeeker was efficient in monitoring wheat N nutrition. NDVI varied depending on the measurement distance, stabilizing from 0.30 to 1.20 m above the canopy. Because of the influence of incident radiation, higher NDVI values were obtained at the beginning and end of the day. The results suggest that for the wheat cultivar Quartzo following soybean under minimum soil disturbance, while there is no need to apply N at sowing, an important increase in grain yield can be obtained with top-dressing N fertilization. In addition, to improve the accuracy in developing N fertilizer recommendation models using GreenSeeker, a consistent protocol for spectral reflectance readings, mainly regarding the time of day, is required.

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Section: Effect Of N Fertilization On Wheat Shoot Dry Matter and Graimentioning

confidence: 99%

Section: Effect Of Sensor Height On Wheat Spectral Reflectance Measurmentioning

confidence: 99%

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

2020

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A Rule Based Classification for Vegetable Production Using Rough Set and Genetic Algorithm

Rathi

Acharjya

2021

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms

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This article describes how agriculture is the main occupation of India, and how the economy depends on agricultural production. Most of the land in India is dedicated to agriculture and people depend on the production of agricultural products. Therefore, forecasting the accuracy of future events based on extracted patterns plays a vital role in improving agricultural productivity. By considering the availability of micronutrients and macronutrients of the soil and water in a particular place, the growth of a plant is determined. This helps people to determine the crops to be cultivated at a certain place. In this article, the forecasting is carried out using rough sets and genetic algorithms. Rough sets are used to produce the decision rules whereas genetic algorithms are used to refine the rules and improve classification accuracy. Accuracy of the classification rules is analyzed using different selection methods and crossover operators. Results show that genetic algorithms with a roulette wheel selection and single point crossover provides better performance when compared with other existing techniques.

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Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks

Cited by 2 publications

References 40 publications

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

A Rule Based Classification for Vegetable Production Using Rough Set and Genetic Algorithm

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