Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.

Monitoring and measurement of carbon dioxide (CO2) is critical for many fields. The gold standard CO2 sensor, the Severinghaus electrode, has remained unchanged for decades. In recent years, many other CO2 sensor formats, such as detection based upon pH-sensitive dyes, have been demonstrated, opening the door for relatively simple optical detection schemes. However, a majority of these optochemical sensors require complex sensor preparation steps and are difficult to control and repeatably execute. Here, we report a facile CO2 sensor generation method that suffers from none of the typical fabrication issues. The method described here utilizes polydimethylsiloxane (PDMS) as the flexible sensor matrix and 1-hydroxypyrene-3,6,8-trisulfonate (HPTS), a pH-sensitive dye, as the sensing material. HPTS, a base (NaOH), and glycerol are loaded as dense droplets into a thin PDMS layer which is subsequently cured around the droplet. The fabrication process does not require prior knowledge in chemistry or device fabrication and can be completed as quickly as PDMS cures (∼2 h). We demonstrate the application of this thin-patch sensor for in-line CO2 quantification in cell culture media. To this end, we optimized the sensing composition and quantified CO2 in the range of 0–20 kPa. A standard curve was generated with high fidelity (R2 = 0.998) along with an analytical resolution of 0.5 kPa (3.7 mm Hg). Additionally, the sensor is fully autoclavable for applications requiring sterility and has a long working lifetime. This flexible, simple-to-manufacture sensor has a myriad of potential applications and represents a new, straightforward means for optical carbon dioxide measurement.

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This study proposed a novel and cost-effective approach to enhance and optimize the polygalacturonase from P. indica. In current investigation, the impact of ammonium sulfate, sugar beet pulp (SBP) and glucose as variables on induction of polygalacturonase from P. indica was optimized using the central composite design (CCD) of response surface methodology (RSM) under submerged fermentation (SmF). Additionally, partial polygalacturonase purification and in situ analysis were performed. The optimal reaction conditions, which resulted in the highest enzyme activity were observed as the following conditions: ammonium sulfate (4 g/L), SBP (20 g/L), glucose (60 g/L). Under the optimized condition, the maximum enzyme activity reached to 19.4 U/ml (127 U/mg) which increased by 5.84 times compared to non-optimized conditions. The partial purified polygalacturonase molecular weight was estimated 60 KDa. In line with the bioinformatic analysis, exo-polygalacturonase sequence of P. indica showed similarity with Rhizoctonia solani’s and Thanateporus cucumeris. These results indicated that SBP act as a cheap and suitable inducer of polygalacturonase production by P. indica in a submerged cultivation. The outcome of this study will be useful for industries to decrease environmental pollution with cost-effective approaches.

This study proposed a novel and cost-effective approach to enhance and optimize the polygalacturonase from P. indica. In current investigation, the impact of ammonium sulfate, sugar beet pulp (SBP) and glucose as variables on induction of polygalacturonase from P. indica was optimized using the central composite design (CCD) of response surface methodology (RSM) under SmF. Additionally, partial polygalacturonase purification and in situ analysis were performed. The optimal reaction conditions, which resulted in the highest enzyme activity were observed as the following conditions: ammonium sulfate (4 g/L), SBP (20 g/L), glucose (60 g/L). Under the optimized condition, the maximum enzyme activity reached to 19.4 U/ml (127 U/mg) which increased by 5.84 times compared to non-optimized conditions. The partial purified polygalacturonase molecular weight was estimated 60 KDa. In line with the bioinformatic analysis, exo-polygalacturonase sequence of P. indica showed similarity with Rhizoctonia solani’s and Thanateporus cucumeris. These results indicated that SBP act as a cheap and suitable inducer of polygalacturonase production by P. indica in a submerged cultivation. The outcome of this study will be useful for industries to decrease environmental pollution with cost-effective approaches.