scholarly journals Internet Of Things-Based Orchid Plant Watering Tool

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Irwan Solikudin ◽  
Syamsudduha Syahrorini

The orchid flower is an ornamental plant that has a lot of interest. For orchid cultivation, there are many things that need to be monitored, including the watering process. Referring to this, an Internet of Things (IoT) -based orchid watering device was made which uses Arduino as a microcontroller, the yl-69 sensor as a soil moisture reader, the DHT11 sensor as a temperature reader, the float swith sensor as an automatic reservoir and an omron timer as a timer for administration. vitamin. All data read by the sensor is sent to the Thingspeak webserver and forwarded to the MIT App Invertor application installed on the smartphone. This tool is made to make it easier for a cultivator to monitor watering plants remotely via an application on a smartphone. After taking data for about two weeks, the sensor readings were obtained, including the yl-69 sensor in wet conditions which has a standard deviation of 0.89 and dry conditions 1.18, the DHT11 sensor has a standard deviation of 0.4 for temperature. For the development of orchid plants, it shows quite good results, it is shown from the stems and leaves of the plants that begin to elongate and grow thick.

2021 ◽  
Author(s):  
Markus Köhli ◽  
Jannis Weimar ◽  
Benjamin Fersch ◽  
Roland Baatz ◽  
Martin Schrön ◽  
...  

<p>The novel method of Cosmic-ray neutron sensing (CRNS) allows non-invasive soil moisture measurements at a hectometer scaled footprint. Up to now, the conversion of soil moisture to a detectable neutron count rate relies mainly on the equation presented by Desilets et al. (2010). While in general a hyperbolic expression can be derived from theoretical considerations, their empiric parameterisation needs to be revised for two reasons. Firstly, a rigorous mathematical treatment reveals that the values of the four parameters are ambiguous because their values are not independent. We find a 3-parameter equation with unambiguous values of the parameters which is equivalent in any other respect to the 4-parameter equation. Secondly, high-resolution Monte-Carlo simulations revealed a systematic deviation of the count rate to soil moisture relation especially for extremely dry conditions as well as very humid conditions. That is a hint, that a smaller contribution to the intensity was forgotten or not adequately treated by the conventional approach. Investigating the above-ground neutron flux by a broadly based Monte-Carlo simulation campaign revealed a more detailed understanding of different contributions to this signal, especially targeting air humidity corrections. The packages MCNP and URANOS were used to derive a function able to describe the respective dependencies including the effect of different hydrogen pools and the detector-specific response function. The new relationship has been tested at three exemplary measurement sites and its remarkable performance allows for a promising prospect of more comprehensive data quality in the future.</p>


2019 ◽  
Vol 8 (2S11) ◽  
pp. 3539-3543

In this present state-of-the-art, Internet of Things (IOT) is an emerging technology that is making our world smarter. WiFi enabled greenhouse monitoring is an intelligent system which is based on several sensors that monitor various changes in temperature, gas concentrations, light and soil moisture in the greenhouse. This comes with an added advantage or provision of linking all these sensors to your mobile phones or computers/laptops using Wi-Fi and internet services through the concept of Internet of Things (IoT), so that if there are any fluctuations, you will be notified immediately. This provides convenient control, through manual operations if necessary, of the greenhouse anytime and anywhere as long as the device is connected to the internet. In this an artificial environment is created so that the crops yield more crops per square meter compared to open field cultivation since the micro climatic parameters that determine crop yield are continuously examined and controlled to ensure that an optimum environment is created.


2021 ◽  
Vol 12 (4) ◽  
pp. 1371-1391
Author(s):  
Raed Hamed ◽  
Anne F. Van Loon ◽  
Jeroen Aerts ◽  
Dim Coumou

Abstract. The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change.


Author(s):  
Jennifer S. Raj ◽  
Vijitha Ananthi J

Green house is generally a building of small or large structures. The structure of the green house is made of walls and the translucent roof, with the capability of maintaining the planned climatic condition. It ensures the growth of plants that requires a specified level of soil moisture, sunlight, humidity and temperature. The green house systems available are human monitored systems that entail the continuous human visit causing distress to the worker and also decrease in the yield if the temperature and the humidity are not properly and regularly maintained. This paves way for the concept of the green house automation. The green house automation formed by the incorporation of the Internet of things and the embedded system addresses the problem faced in the green house and provides with the automated controlling and monitoring of the green house environment replacing the undeviating administration of the farmers. This paper also proposes the automation using internet of things in green house environment by using the Netduino 3 and employing the sensors for the sensing the moisture, temperature, sunlight and humidity, to enhance the production rate and minimize the discomfort caused to the farmers.


2021 ◽  
Author(s):  
Johannes Vogel

<p>The ecosystems of the Mediterranean Basin are particularly prone to climate change and related alterations in climatic anomalies. The seasonal timing of climatic anomalies is crucial for the assessment of the corresponding ecosystem impacts; however, the incorporation of seasonality is neglected in many studies. We quantify ecosystem vulnerability by investigating deviations of the climatic drivers temperature and soil moisture during phases of low ecosystem productivity for each month of the year over the period 1999 – 2019. The fraction of absorbed photosynthetically active radiation (FAPAR) is used as a proxy for ecosystem productivity. Air temperature is obtained from the reanalysis data set ERA5 Land and soil moisture and FAPAR satellite products are retrieved from ESA CCI and Copernicus Global Land Service, respectively. Our results show that Mediterranean ecosystems are vulnerable to three soil moisture regimes during the course of the year. A phase of vulnerability to hot and dry conditions during late spring to midsummer is followed by a period of vulnerability to cold and dry conditions in autumn. The third phase is characterized by cold and wet conditions coinciding with low ecosystem productivity in winter and early spring. These phases illustrate well the shift between a soil moisture-limited regime in summer and an energy-limited regime in winter in the Mediterranean Basin. Notably, the vulnerability to hot and dry conditions during the course of the year is prolonged by several months in the Eastern Mediterranean compared to the Western Mediterranean. Our approach facilitates a better understanding of ecosystem vulnerability at certain stages during the year and is easily transferable to other study areas and ecoclimatological variables.</p>


Author(s):  
Alessandra Dutra Coelho ◽  
Bruno Guilherme Dias ◽  
Wanderson de Oliveira Assis ◽  
Fernando de Almeida Martins ◽  
Rogerio Cassares Pires

1985 ◽  
Vol 105 (3) ◽  
pp. 535-541 ◽  
Author(s):  
D. C. E. Wurr ◽  
Jane R. Fellows ◽  
L. P. Bufton

SUMMARYPelleted seed of the crisp lettuce variety Pennlake was sown on five occasions with units from either the experimental dibber drill designed by the National Institute of Agricultural Engineering or a Stanhay S870 drill. There were three dibber-drill treatments: seeds left in open holes or covered with peat–vermiculite or perlite. Seedling emergence and growth from each sowing of the four drill treatments were compared under different moisture regimes.There were large differences between drill treatments in emergence percentage, time to emergence of 50% of the seedlings which emerged (t50) and seedling weight at all sowings but there was a significant effect of drill treatment on the standard deviation of seedling emergence times at only one sowing. Of the dibber-drill treatments, only very occasionally did open holes or perlite cover give significantly better emergence than peat-vermiculite cover, which gave the most consistent results. In general, the dibber drill with peat-vermiculite cover gave significantly faster and higher emergence and heavier seedlings than the Stanhay drill. Differences in percentage seedling emergence tended to be greater under dry conditions.


2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090782
Author(s):  
Shi Qinglan ◽  
Shi Yujiao ◽  
Liu Xiaochen ◽  
Mei Shuli ◽  
Feng Lei

The multilayer soil moisture Internet of things sensor is designed to monitor the moisture of multiple soil profiles in real time. Its sensitivity and accuracy are of great concern to improve the performance of sensors. This article introduces the system composition of the end-cloud integrated multilayer soil moisture Internet of things sensor and then focuses on the design of key technologies, such as the moisture detection circuit, the time division multiplexing detection technology, and the deredundancy circuit in the analog–digital integrated design. The performance of the soil moisture detection circuit is directly related to the measurement accuracy of the sensor. A detection method is proposed using a high-frequency double-resonance circuit, which can detect small changes in moisture by changing the circuit detuning voltage. The maximum root mean square error of the calibration is less than 1.35% for five typical soils from different places. Compared with that of an independent detection method, the output consistency of the time division multiplexing detection is significantly improved by using the time division multiplexing detection method, which has a root mean square error of only 0.12%. In order to reduce errors caused by inconsistency in each burial, the gravimetric analysis is used in the sensitivity monitoring test, which shows that small changes in soil moisture can be detected by the circuit.


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