Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime.

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture.

The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.

Sustained enhancement of photosynthesis in coffee trees grown under free-air CO2 enrichment conditions: disentangling the contributions of stomatal, mesophyll, and biochemical limitations.

Coffee (Coffea spp.), a globally traded commodity, is a slow-growing tropical tree species that displays an improved photosynthetic performance when grown under elevated atmospheric CO2 concentrations ([CO2]). To investigate the mechanisms underlying this response, two commercial coffee cultivars (Catuaí and Obatã) were grown using the first free-air CO2 enrichment (FACE) facility in Latin America. Measurements were conducted in two contrasting growth seasons, which were characterized by the high (February) and low (August) sink demand. Elevated [CO2] led to increases in net photosynthetic rates (A) in parallel with decreased photorespiration rates, with no photochemical limitations to A. The stimulation of A by elevated CO2 supply was more prominent in August (56% on average) than in February (40% on average). Overall, the stomatal and mesophyll conductances, as well as the leaf nitrogen and phosphorus concentrations, were unresponsive to the treatments. Photosynthesis was strongly limited by diffusional constraints, particularly at the stomata level, and this pattern was little, if at all, affected by elevated [CO2]. Relative to February, starch pools (but not soluble sugars) increased remarkably (>500%) in August, with no detectable alteration in the maximum carboxylation capacity estimated on a chloroplast [CO2] basis. Upregulation of A by elevated [CO2] took place with no signs of photosynthetic downregulation, even during the period of low sink demand, when acclimation would be expected to be greatest.

Soil calcium and pH monitoring sensor system.

An agrarian sensorial system based on temperature, moisture, and all solid-state ion-selective potentiometric sensors was developed with the objective of monitoring the behavior of H+ and Ca2+ ions in soil and in real conditions, contributing with a new tool that tries to complement the current precision agriculture technology. The evaluation of the sensorial system to pH monitoring presented a good correlation between the results obtained by the system and the standard methodology, allowing us to notice the soil buffer capacity at different soil depths. With regard to calcium, the sensor system also presented an agreement between its results and those obtained by flame atomic absorption spectrometry, using a calibration model based on multiple linear regressions that allows the correct determination of Ca2+ concentrations in soil depths where the relative moisture is different. In this way, using well-known potentiometric sensors in a complex, discontinued, and heterogeneous matrix, such as soil, the sensorial system proved to be a useful task for agrochemical field applications.

In-soil potassium sensor system.

A potentiometric sensor system based on potassium ion-selective electrodes was developed for agricultural purposes. Sensors were built using PVC ion-selective membranes over an inner solid contact prepared with graphite-epoxy composites. A copper plate was used as a reference electrode. A two-stage electronic circuit composed of current and voltage amplifiers was designed to interface the sensors to a distributed data acquisition system. Three ion-selective sensors and three off-the-shelf temperature sensors and their associated circuits were mounted in a PVC tube to set up a soil probe. The electronic controls were placed in an airtight box fixed at the upper part of the probe. The system was evaluated in the field, where the sensors presented sensibility within the range of 69-71 mV dec(-)(1). Extracts of soil samples were analyzed by a current flame photometry approach, and the results, compared with the probe measurements, showed a linear relationship (r (2) = 0.992 and 0.995, respectively, to 5 and 20 cm depths), which implies viability and instrumentation reliability for agricultural applications.