Optimization of Selection of the Machinery Park in Sustainable Agriculture

Cupiał, M.; Kowalczyk, Z.

doi:10.3390/su12041380

Cited by 10 publications

(12 citation statements)

References 36 publications

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“…Timely monitoring of agricultural production and accurate prediction of crop yield are crucial due to the severe economic and social consequences of food shortages [1,2]. Especially, agricultural products have to be increased by 70% by 2050, when the world population is expected to reach 9 billion people [3].…”

Section: Introductionmentioning

confidence: 99%

“…To date, the spectral features at the flowering stage has not been considered in rice yield estimation, although some researchers have observed that the presence of flowers can cause canopy spectral changes in certain bands [29,30]. At present, research on remote sensing monitoring of flowering can be categorized into three types: (1) the extraction of floret amount information (flower coverage ratio and flower number) basing on remote sensing data. Thorpa et al [31] estimated the floret number of leskelaole rape by flower coverage proportion.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, UAV equipped with hyperspectral imaging sensors are used to obtain hyperspectral images that cover the whole flowering stage of rice, and new florescence spectral indices were proposed based on spectral characteristics before and after flowering, and then the capabilities of these florescence spectral indices in improving the performance of VIs-based yield estimation models were tested. The objectives of this study were: (1) to determine the characteristic date of the flowering stage and sensitive bands for rice yield estimates by analyzing spectral information of the flowering stage;…”

Section: Introductionmentioning

confidence: 99%

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Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

et al. 2021

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Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

et al. 2021

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“…The most commonly proposed approach to the design of the machine-tractor fleet is based on heuristic methods and is very laborious [4]. In some Eastern European countries, it is proposed to determine the necessary equipment on the basis of the so-called reference tractor or using specific software [5][6][7]. Unfortunately, these methods are not suitable for small farms due to the high inaccuracy of input information, the use of aggregated economic indicators or the unavailability of relevant application programs.…”

Section: Introductionmentioning

confidence: 99%

Approach to design sequence of field melliferous crops taking into account use of agricultural machinery

Atanasov

Vezirov

2021

20th International Scientific Conference Engineering for Rural Development Proceedings

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An approach for mutually beneficial solving the issues of providing melliferous plants for honey bees and optimizing the necessary equipment and its use is presented. The plants must be included in appropriate crop rotations and pollinated by bees. To ensure strong bee colonies, field crops, varieties and hybrids with high potential nectar yields, overlapping flowering periods, with different vegetation durations and different sowing dates are offered. At the same time, appropriate land management projects take into account climatic and meteorological conditions, too. Possible solutions are illustrated with graphs of expected nectar productivity, need for machinery, staff, fuel in a production year. The effectiveness of the proposed approach is reported in two directions: on the one hand, the benefit for farmers growing agricultural crops is shown in terms of reducing the need for machinery, increasing the employment of service personnel for socially, economically and environmentally sustainable agriculture. On the other hand, the benefit for beekeepers corresponding area is expressed in providing pollen and nectar for bees throughout the honey harvest period, good stocking of bee colonies with honey, reduction of harmful effects of overpopulation with bee colonies, lower feeding costs, for transport and maintenance, high yields of bee products. The approach is suitable for application in flat-hilly areas with predominantly arable agricultural lands and, at the same time, with insignificant areas covered by forest and meadow flowering vegetation.

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“…Assessment of machinery and operator costs could be estimated for each field, or subsection, instead of using some type of aggregated average. Applying conventional machinery management models with DGPS data for round baling operations provides the basis for analysis and simulation of the harvest of high-yielding bioenergy crops [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

In-Field Performance of Biomass Balers

2020

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Herbaceous biomass will contribute significantly to meeting renewable energy goals. Harvesting equipment for hay is generally suitable for mowing, raking, and baling grasses such as switchgrass; however, there is a need for field data to better understand machine performance in energy crops. The purpose of this study was to collect field data to estimate baler field capacity, throughput, and speed. Data gathered with a Differential Global Positioning System (DGPS) unit during baling provided time-motion studies of baler productivity. Six fields were used to compare field capacity, speed, and throughput results from four round balers and one large-square baler. The results show that in-field performance of round balers is significantly affected by yield, but that the relationship can be represented with machinery management concepts, knowledge of maximum throughput, and wrap-eject time. Baler performance will be overestimated if the yield, maximum throughput, and wrap-eject time are not correctly accounted for.

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Optimization of Selection of the Machinery Park in Sustainable Agriculture

Cited by 10 publications

References 36 publications

Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

Approach to design sequence of field melliferous crops taking into account use of agricultural machinery

In-Field Performance of Biomass Balers

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