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Computer Control Systems Used in Precision Agriculture
Abstract
The paper presents a computer system for monitoring plant growth, developed for the needs of precision agriculture for small agricultural areas. The work contains a description of the monitoring system with a breakdown into the key elements of the process. An exemplary method of preparing orthophotomaps of the area was presented. The method of making maps that can be implemented on a PC computer has been described. The paper describes the most frequently used Vegetation Index. A test of determining the coefficients was carried out on an exemplary aerals with an area of 5.28 ha. Typical positioning systems for agricultural machines are discussed. The DGPS navigation method was used in the tests. Tests have confirmed that it can be used in precision agriculture with small aerals. The solution is optimal in terms of positioning accuracy and economics of small farms. The presented system was tested during one cycle of vegetation of winter barley sown with the no-plowing method. On this basis, the complexity of the system was assessed and its implementation was proposed. The proposed solution does not require complex computer systems. It has been designed so that it can be implemented on standard PC equipment cooperating with a short-range drone equipped with a standard RGB camera.KeywordsPrecision agricultureField mappingVegetation indicatorGeological indicatorPrecision GPS
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Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS R2c for C = 0.90 and N = 0.96 vs. HSI R2c for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.
Conservation agriculture has three main pillars, i.e., minimum tillage, permanent soil cover, and crop rotation. Covering the soil surface with plant residues and minimum mechanical soil disturbance can all result from introducing a strip-till one-pass (ST-OP) system. The aim of this study was to determine the impact of the ST-OP technology on the management of plant residues, soil properties, inputs, and emissions related to crop cultivation. We compared the effect of a ST-OP system against conventional tillage (CT) using a plough, and against reduced, non-ploughing tillage (RT). Four field experiments were conducted for evaluating the covering of soil with plant residues of the previous crop, soil loss on a slope exposed to surface soil runoff, soil structure and aggregate stability, occurrence of soil organisms and glomalin content, soil moisture and soil water reserve during plant sowing, labour and fuel inputs, and CO2 emissions. After sowing plants using ST-OP, 62.7–82.0% of plant residues remained on the soil surface, depending on the previous crop and row spacing. As compared with CT, the ST-OP system increased the stability of soil aggregates of 0.25–2.0 mm diameter by 12.7%, glomalin content by 0.08 g·kg−1, weight of earthworms five-fold, bacteria and fungi counts, and moisture content in the soil; meanwhile, it decreased soil loss by 2.57–6.36 t·ha−1 year−1, labour input by 114–152 min·ha−1, fuel consumption by 35.9–45.8 l·ha−1, and CO2 emissions by 98.7–125.9 kg·ha−1. Significant favourable changes, as compared with reduced tillage (RT), were also found with respect to the stability index of aggregates of 2.0–10.0 mm diameter, the number and weight of earthworms, as well as bacteria and fungi counts.
Vegetation monitoring
is a task that requires much time and human effort, but by using an unmanned aerial vehicle with a system that can
store captured data digitally, the task can
be more manageable and efficient. Past research has shown many formulas were
developed by researchers to capture vegetation data in varying conditions and equipment. This paper
discusses an experiment conducted to test three of those formulas using visible
band data images. The formulas are the visible atmospherically resistant index,
the green leaf index, and the visible atmospherically resistant indices green.
The objective of this paper is to report and discuss our findings from
experiments conducted using each formula as well as to compare the accuracy of these formulas.
Sustainable agriculture integrates three main goals; environmental health, economic profitability, and social and economic equity (Feenstra, 2012) and is an alternative way of production in an ecological way. Agricultural mechanization involves the use of tools and machine to improve efficiency of human time and labor without degrading quality of the soil or other factors of production and affects the sustainability since it is directly related with the environmental, economic and social issues. Agricultural technologies include both engineering and biological inventions and discoveries, such as modifications to machinery, the physical environment or biological components of a system (Sassenrath, G.F. et. al., 2008). The advances in these technologies that involved in use of computers and sensors have increased yield and reduced the use of inputs and shaped agriculture while improvements in mechanization have increased production output with fewer people and improved the safety of farm workers. Reducing energy requirements on the other hand is another concern since energy means fossil fuel which is a non-renewable source. Direct seeding or reduced tillage operations are also becoming widespread in the world in order to reduce the energy requirement while maintaining a better soil structure. The environmental concerns change the concepts in agricultural production. One of the changes in concepts is variable rate fertilizer and spraying applications since these agricultural operations are directly related to environmental issues along with the use of inputs efficiently. Hence, the future production systems are expected to be dynamic in order to adopt climatic changes. The objective of this paper was to reveal the technological advancements and transitions for sustainable agricultural production.
The application of spectroradiometric index such as the normalized difference vegetation index (NDVI) to assess green biomass or nitrogen (N) content has focused on the plant canopy in precision agriculture or breeding programs. However, little is known about the usefulness of these techniques in isolated plants. The few reports available propose the use of a spectro-radiometer in combination with special adaptors that improve signal acquisition from plants, but this makes measurements relatively slow and unsuitable. Here we studied the direct use (i.e. without adaptors) of a commercial cost-effective spectroradiometer, GreenSeeker TM (NTech Industries Ins., Ukiah, California, USA) provided with an active sensor (i.e. equipped with its own source of radiation) for measuring NDVI in four genotypes of durum wheat (Triticum turgidum L. var. durum) grown in pots under a range of water and N regimes. Strong correlations were observed between NDVI measurements and dry aboveground biomass (AB), total green area (TGA), green area without spikes (GA) and aboveground N content (AN). To prove the predictive ability of NDVI measured under potted conditions, linear re-gression models for each growth trait and for plant N content were built with the data of two genotypes. The models accurately predicted growth traits and N content, confirming the direct relationship between total plant biomass and spectroradiometric readings.
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference vegetation index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference vegetation index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a “vegetation signal” to “soil noise” ratio.
This article presents the research project, the realization of which was started in 2006. Its gist refers to the pussibilities of composting animal waste products. Animal waste products intended to compost within the confines of research project are the material of category III, especially food waste products from the households as well as waste products from the butcheries. The process of composting the above-mentioned waste products has been prepared on basis of closed technology with modified bioreactor. Biodegradable plant waste has been the input mass of such a rector. In order to modify the process in bioreactor, an intensive decomposition was completed with an intensive hygiene improvement as a result of placing in work space of bioreactor the monitoring system of temperature with time. The article has described the research method and the results expected.
A novel method of calculating a solid velocity field based on a twin-plane electrical capacitance system is presented. This method introduces the concept of the best-correlated pair of pixels instead of the classical assumption that a flow pattern between two sensor planes is frozen. Preliminary results and future applications of the proposed method are discussed.
Monitoring of typical field work in different soil conditions using remote sensing - a literature review and some concepts for the future
- J Barwicki
- K Mazur
- W J Wardal
- M Majchrzak
- K Borek
Aygün, İ: Advancements and transitions in technologies for sustainable agricultural production
- H Ü Evcim
- A Değirmencioğlu
- E G Özgünaltay
- HÜ Evcim
Winter wheat and spring barley canopies under strip-till one-pass technology
- I Jaskulska
- D Jaskulski
Sustainable agriculture: and introduction to extensive and intensive agriculture
- P Sharifi