Python Software Integrates with Microcontrollers and Electronic Hardware to Ease Development for Open-Source Research and Scientific Applications

Many options exist for developing and implementing monitoring systems for research and scientific applications. Commercially, available systems and devices, however, are usually built using proprietary tools and programming instructions, and often offer limited flexibility for end users. The use of open-source hardware and software has been embraced by the research and scientific communities and can be used to target unique data and information requirements. Development based on the Arduino microcontroller project has resulted in many successful applications, and the Arduino hardware and software environment continues to expand and become more powerful but can be intimidating for users with limited electronics or programming experience. The open-source Python language has gained in popularity and is being taught in schools and universities as an introduction to computer programming and software development due to its simple structure, ease of use, and large standard library of functions. A project called CircuitPython was developed to extend the use of Python to programming hardware devices such as programmable microcontrollers and maintains much of the original Python lang<span>uage and features, with additional support for accessing and controlling microcontroller hardware. The objective of the work reported here is to discuss the CircuitPython programming language and demonstrate its use in the development of research and scientific applications. Several open-source sensing and monitoring systems developed using open-source hardware and the open-source CircuitPython programming language are presented and described.

Evaluation of alternative methods for estimating reference evapotranspiration

Evapotranspiration is an important component in water-balance and irrigation scheduling models. While the FAO-56 Penman-Monteith method has become the de facto standard for estimating reference evapotranspiration (ETo), it is a complex method requiring several weather parameters. Required weather data are oftentimes unavailable, and alternative methods must be used. Three alternative ETo methods, the FAO-56 Reduced Set, Hargreaves, and Turc methods, were evaluated for use in Mississippi, a humid region of the USA, using only measurements of air temperature. The Turc equation, developed for use with measured temperature and solar radiation, was tested with estimated radiation and found to provide better estimates of FAO-56 ETo than the other methods. Mean bias errors of 0.75, 0.28, and -0.19 mm, mean absolute errors of 0.92, 0.68, and 0.62 mm, and percent errors of 22.5%, 8.5%, and -5.7% were found for daily estimates for the FAO-56 Reduced Set, Hargreaves, and Turc methods, respectively.

Irrigation Methods and Scheduling in the Delta Region of Mississippi: Current Status and Strategies to Improve Irrigation Efficiency

Even though annual rainfall is high in the Delta region of Mississippi, only 30% occurs during the months in which the major crops are produced, making irrigation often necessary to meet crop water needs and to avoid risk of yield and profitability loss. Approximately, 65% of the farmland in this region is irrigated. The shallow Mississippi River Valley Alluvial Aquifer is the major source of water for irrigation and for aquaculture in the predominant catfish industry. This groundwater is being heavily used as row-crop irrigation has increased tremendously. Water level in this aquifer has declined significantly over the past twenty five years, with overdraft of approximately 370 million cubic meters of water per year. Moreover, the common irrigation practices in the Delta re-gion of Mississippi do not use water efficiently, further depleting the ground water and making ir-rigation more expensive to producers due to increasing energy prices. Irrigation experts in the re-gion have tested and verified various methods and tools that increase irrigation efficiency. This article presents a review of the current status of the irrigation practices in the Delta region of Mis-sissippi, and the improved methods and tools that are available to increase irrigation efficiency and to reduce energy costs for producers in the region as well as to stop the overdraft of the declining aquifer, ensuring its sustainable use.

Open-Source Hardware Is a Low-Cost Alternative for Scientific Instrumentation and Research

Scientific research requires the collection of data in order to study, monitor, analyze, describe, or understand a particular process or event. Data collection efforts are often a compromise: manual measurements can be time-consuming and labor-intensive, resulting in data being collected at a low frequency, while automating the data-collection process can reduce labor requirements and increase the frequency of measurements, but at the cost of added expense of electronic data-collecting instrumentation. Rapid advances in electronic technologies have resulted in a variety of new and inexpensive sensing, monitoring, and control capabilities which offer opportunities for implementation in agricultural and natural-resource research applications. An Open Source Hardware project called Arduino consists of a programmable microcontroller development platform, expansion capability through add-on boards, and a programming development environment for creating custom microcontroller software. All circuit-board and electronic component specifications, as well as the programming software, are open-source and freely available for anyone to use or modify. Inexpensive sensors and the Arduino development platform were used to develop several inexpensive, automated sensing and datalogging systems for use in agricultural and natural-resources related research projects. Systems were developed and implemented to monitor soil-moisture status of field crops for irrigation scheduling and crop-water use studies, to measure daily evaporation-pan water levels for quantifying evaporative demand, and to monitor environmental parameters under forested conditions. These studies demonstrate the usefulness of automated measurements, and offer guidance for other researchers in developing inexpensive sensing and monitoring systems to further their research.

Effect of Shade on Seed Protein, Oil, Fatty Acids, and Minerals in Soybean lines Varying in Seed Germinability in the Early Soybean Production System

The cause of poor seed quality (germination) of soybean produced in the Early Soybean Production System in the midsouth U.S.A. is still not completely understood. Temperature, solar radiation, and soybean genotype may all be important factors involved. The objective of this research was to evaluate seed composition, mineral level, and nitrogen assimilation in high and low germinability soybean plant introductions in a full-sunlight (unshaded) and a low-light intensity (shaded) environments. Shade netting was employed in field plots that reduced light intensity by about 50% and air temperature by about 10?F/6?C (10%). A two-year field experiment was conducted on soybean accessions with high germinability (PI 587982A and PI 603723), low germinability (PI 80480 and PI 84976-1), and on soybean cultivars (DSR-3100 RR STS and Pella 86). Results showed that shade resulted in higher total oil, linoleic and linolenic acids, and lower protein and oleic acid. Shade also resulted in lower nitrogen assimilation, leaf chlorophyll concentration, but unshade resulted in higher total seed boron, iron, and nitrogen concentrations. Seed structural boron was positively correlated with germination and protein. Structural boron percentage was consistently higher under shade than under unshade in PI 80480, PI 84976-1, DSR-3100 RR STS, and Pella 86, but consistently higher under unshade than under shade in higher germinability lines PI 587982A and PI 603723, suggesting that different distribution mechanisms of structural boron exist between genotypes. The positive correlation between germination and structural B and between protein and structural B signify a possible role of B in seed quality traits. Our research demonstrated that light intensity, combined with temperature, can alter seed constituents. Higher germinability lines had higher germination rates and lower percentages of hardseededness, desirable traits for soybean seed.

Influence of Planting Date on Seed Protein, Oil, Sugars, Minerals, and Nitrogen Metabolism in Soybean under Irrigated and Non-Irrigated Environments

Information on the effect of planting date and irrigation on soybean [Glycine max (L.) Merr.] seed composition in the Early Soybean Production System (ESPS) is deficient, and what is available is inconclusive. The objective of this research was to investigate the effects of planting date on seed protein, oil, fatty acids, sugars, and minerals in soybean grown under irrigated (I) and non-irrigated (NI) conditions. A 2-yr field experiment was conducted in Stoneville, MS in 2007 and 2008. Soybean was planted during second week of April (early planting) and second week of May (late planting) each year. Results showed that under irrigated condition, early planting increased seed oil (up to 16% increase) and oleic acid (up to 22.8% increase), but decreased protein (up to 6.6% decrease), linoleic (up to 10.9% decrease) and linolenic acids (up to 27.7% decrease) compared to late planting. Under I conditions, late planting resulted in higher sucrose and raffinose and lower stachyose compared with early planting. Under NI conditions, seed of early planting had higher protein (up to 4% increase) and oleic acid (up to 25% increase) and lower oil (up to10.8% decrease) and linolenic acids (up to 13% decrease) than those of late planting. Under NI, stachyose concentration was higher than sucrose or raffinose, especially in early planting. Under I, early planting resulted in lower leaf and seed B, Fe, and P concentrations compared with those of late planting. Under NI, however, early planting resulted in higher accumulation of leaf B and P, but lower seed B and P compared with those of late planting. This research demonstrated that both irrigation and planting date have a significant influence on seed protein, oil, unsaturated fatty acids, and sugars. Our results suggest that seed of late planting accumulate more B, P, and Fe than those of early planting, and this could be a beneficial gain. Limited translocation of nutrients from leaves to seed under NI is undesirable. Soybean producers may use this information to maintain yield and seed quality, and soybean breeders to select for seed quality traits and mineral translocation efficiency in stress environments.

Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

There is a growing interest in the Open Ag community to use inexpensive sensors controlled by open-source software to measure plant height and plant canopy temperature of agricultural crops. Plant height and plant canopy temperature are key indicators of plant health. This research study reports on an ongoing research initiative to test a compact and inexpensive mobile sensor to measure plant height and plant canopy temperature. The system is controlled by open source software and hardware. The specific objectives for this study were to analyze the relationship between plant height and plant canopy temperature of soybeans (Glycine max L.) measured with the mobile system and to analyze the spatial correlation of the plant height and plant canopy temperature measurements. Data were collected in a soybean plot in 2018 and 2019. Descriptive statistics, Pearson correlation, and geostatistical techniques were used to evaluate the data. A negative statistically significant (p ≤ 0.05) relationship was observed between the plant height and the plant canopy temperature measurements (r = &#8722;0.54, 2018;r = &#8722;0.37, 2019). Also, both parameters were spatially correlated;however, plant height had a greater spatial continuity than plant canopy temperature. Furthermore, similar patterns were observed for the in-field variability of the plant height and plant temperature maps derived via kriging. Similarities in plant height and plant canopy temperatures were observed from one year to the next, suggesting that the sensor technologies could be used as a historical record for monitoring growth patterns in soybean fields. The sensors and techniques used in this study can be easily adapted to other crops, thus providing two important layers for monitoring plant growth and potentially plant stress.

Evolving Open-Source Technologies Offer Options for Remote Sensing and Monitoring in Agriculture

A variety of sensing and monitoring systems have been developed based on the concept of open-source and on open-source hardware and software components. Availability and relatively low cost of hardware components and availability and ease of use of software components allow access to sensing and monitoring technologies that were previously unattainable to many potential users. Advances in electronic monitoring and evolving cellular communications technologies are increasingly offering more, simpler, and less expensive options for remote monitoring. Due to the near-future cessation of 2G and 3G cellular network services, however, many existing monitoring systems will need to be redesigned to operate on alternative cellular networks. A soil-moisture monitoring system was developed incorporating updated open-source Arduino microcontrollers and the recently introduced LTE Cat-M1 cellular network to transmit sensor measurements via the cellular network for access on an internet website. The monitoring system costs approximately US$130 to construct the electronic circuitry and less than US$1 per month for cellular network access and data transmission. Data were transmitted with a 95% success rate, and the monitoring system operated continuously throughout an entire crop growing season with no battery recharge or maintenance requirements. The design and operation of the monitoring system can serve as a basis for other remote monitoring systems.

A Miniature Sensor for Measuring Reflectance, Relative Humidity, and Temperature: A Greenhouse Example

There is a growing interest in using miniature multi-sensor technology to monitor plant, soil, and environmental conditions in greenhouses and in field settings. The objectives of this study were to build a small multi-channel sensing system with ability to measure visible and near infrared light reflectance, relative humidity, and temperature, to test the light reflectance sensors for measuring spectral characteristics of plant leaves and soilless media, and to compare results of the relative humidity and temperature sensors to identical measurement obtained from a greenhouse sensor. The sensing system was built with off-the-shelf miniature multispectral spectrometers and relative humidity and temperature sensors. The spectrometers were sensitive to visible, red-edge, and near infrared light. The system was placed in a greenhouse setting and used to obtain relative reflectance measurements of plant leaves and soilless media and to record temperature and relative humidity conditions in the greenhouse. The spectrometer data obtained from plant leaf and soilless media were compatible with baseline spectral data collected with a hyperspectral spectroradiometer. The greenhouse was equipped with a relative humidity and temperature sensor. The relative humidity and temperature sensor measurements from our sensor system were strongly correlated with the relative humidity and temperature results obtained with the greenhouse sensors...

Soybean seed protein, oil, and fatty acids are altered by S and S + N fertilizers under irrigated or non-irrigated environments

Information on the effect of sulfur (S) or sulfur+nitrogen (S + N) on soybean seed composition is scarce. Thus, the objective of this study was to investigate the effects of S, and S + N fertilizers on soybean [(Glycine max (L.) Merr.)] seed composition in the Early Soybean Production System (ESPS) under irrigated (I) and nonirrigated (NI) environments. Two separate field experiments were conducted from 2005 to 2007. One experiment was irrigated, and the second experiment was nonirrigated. Under I condition, S at a rate of 44.8 kg/ha alone or with N at 112 kg/ha resulted in a consistent increase in seed protein and oleic acid concentrations, and a decrease in oil and linolenic acid concentrations compared with the control (C). For example, in 2006 and compared with the C, application of S + N increased the percentage up to 11.4% and 48.5% for protein and oleic acid, respectively. However, oil concentration decreased by 3%. Protein and oleic acid increase were accompanied by a higher percentage of leaf and seed N and S. Under NI conditions, seed protein and oleic acid concentrations were significantly higher in C than in any S or S + N treatments, but the oil and linolenic acid concentrations were significantly lower. The results indicate that specific rate of S alone or S + N combined can alter seed composition under irrigated or nonirrigated conditions. This knowledge may help plant breeders to develop and release cultivars to suit specific target locations to grow new value-added soybeans or select for specific seed composition traits under specific environmental stress factors such as drought.

Development of an Open-Source Cloud-Connected Sensor-Monitoring Platform

Rapid advances in electronics and communications technologies offer continuously evolving options for sensing and awareness of the physical environment. Many of these advances are becoming increasingly available to “non-professionals,” that is, those without formal training or expertise in disciplines such as electronic engineering, computer programming, or physical sciences, via the open-source concept. The open-source concept of collaboration and sharing of ideas offers advantages including low cost, ease of use, extensive array of electronic technologies offered, and technical and programming support. Expansion of communications infrastructure, including wireless, cellular, and internet networks, continues to provide greater ability to be connected and share information over any distance in real time. A basic data-collection platform using open-source hardware and software and internet cloud components was developed and discussed. The simple and inexpensive platform was used to develop and implement an instrument system to remotely monitor soil-moisture status in agricultural fields. The monitoring system transferred data regularly from the field to an internet website via the cellular communications network. The system performed reliably over an entire growing season with no maintenance requirements. The basic platform can be modified to suit a user’s specific requirements, and offers options for automated collection, viewing, and sharing of remotely sensed data.

Rapid Deployment of Internet-Connected Environmental Monitoring Devices

Advances in electronic sensing and monitoring systems and the growth of the communications infrastructure have enabled users to gain immediate access to information and interaction with physical devices. To facilitate the uploading, viewing, and sharing of data via the internet, while avoiding the complexities and expense of creating personal web servers, a number of service providers have created websites offering free data hosting and viewing capabilities. Simple setup and configuration routines and available application programming interfaces allow users to quickly and easily interface sensing and monitoring devices to the internet. To demonstrate the ease and feasibility of deploying internet-connected devices, an urban landscape environmental monitoring system, consisting of two wireless field sensing systems and a wireless central receiver, was developed. The field-data sensing units consisted of Arduino microcontroller platforms, sensors, and Xbee radio modems, while the receiver consisted of an Arduino microcontroller, Xbee radio, and Ethernet module connected to an internet modem. The receiver collected and stored data from the wireless transmitters, and uploaded the data to the Xively Cloud Services data hosting and sharing website. Real-time and archived sensor data were then made available for public viewing via the internet and a web browser. Data-sharing services such as Xively provide rapid and convenient means of developing internet-accessible data-collection and viewing applications, enabling enhanced access to the Internet of Things.

Corn Yield Response to Reduced Water Use at Different Growth Stages

To develop an efficient water use strategy for crop irrigation, we need to know how much water can be reduced without decreasing yield. A study was designed to determine corn growth stages at which water could be reduced without affecting grain yield, and at what soil moisture level water deficit stress begins in the plants in a silt loam soil. An experiment was conducted in a randomized complete block with a 3 × 4 factorial design in four replications, where treatments consisted of three soil moisture levels [100%, 75%, and 50% of field capacity (FC) of a silt loam soil by weight] and four growth stages [fourteen leaf stage (V14), silking (R1), milk (R3), and dent (R5) stages] in a greenhouse. Growth stages at the reproductive and grain fill stages of corn were selected because this study was intended for the Mississippi Delta, where there is frequent drought during these growth stages making irrigation necessary for corn production, whereas there is usually adequate rainfall during the vegetative growth stages. Results from this study showed that reducing soil moisture from 100% FC (fully irrigated) to 75% FC of a silt loam soil starting at the R1 growth stage in corn did not reduce yield significantly compared to yield from the 100% FC, while saving a significant amount of water. Physiological investigations at the three soil moisture treatments showed that a mild moisture deficit stress might have started at the 75% FC treatment. With further investigation, if savings in water at 75% FC result in a significant reduction in energy cost, it may be profitable to reduce soil moisture to 75% FC in a silt loam soil.

Testing an Open-Source Multi Brand Sensor Node to Monitor Variability of Environmental Conditions inside a Greenhouse

A research project was undertaken to collect data to study the variability in environmental parameters inside a greenhouse. The specific objectives of the project were to 1) develop the network of open-source sensor nodes, 2) evaluate the performance of the individual sensors, and 3) quantify the spatial variability of environmental parameters within the greenhouse. The sensor system consisted of a sensor node equipped with three temperature and relative humidity sensors, one light-level sensor, one barometric pressure sensor, AA batteries, and a microcontroller board with a built-in radio to transfer the data wirelessly. The sensors were controlled with open-source technology. Twelve sensor nodes were fabricated and placed at different locations in a greenhouse to evaluate variability in sensor location and environmental parameters. Data collected during February 2019 were used to test the sensors. Heatmaps were employed to assess the variability of the measurements. Variability in greenhouse temperature, relative humidity, and light level conditions was identified with the sensor system. Overall, environmental measures based on time of day appeared to be a better grouping mechanism for analysis than sensor location in the greenhouse. Similar patterns were observed between the different sensor manufacturer’s heatmaps for the temperature sensors and relative humidity sensors. This study provided a protocol for developing the inexpensive multi-sensor sensor node and showed that automated measurements obtained with the system could help monitor variation in a greenhouse setting. The costs of the system components fabricated for this study included US$76 for each sensor node and US$55 for the gateway, totaling US$967 for the 12-node study described.

Mobile Open-Source Plant-Canopy Monitoring System

Many agricultural applications, including improved crop production, precision agriculture, and phenotyping, rely on detailed field and crop information to detect and react to spatial variabilities. Mobile farm vehicles, such as tractors and sprayers, have the potential to operate as mobile sensing platforms, enabling the collection of large amounts of data while working. Open-source hardware and software components were integrated to develop a mobile plant-canopy sensing and monitoring system. The microcontroller-based system, which incorporated a Bluetooth radio, GPS receiver, infrared temperature and ultrasonic distance sensors, micro SD card storage, and voltage regulation components, was developed at a cost of US$292. The system was installed on an agricultural vehicle and tested in a soybean field. The monitoring system demonstrates an application of open-source hardware to agricultural research and provides a framework for similar or additional sensing applications.