Location: Sugarcane Field Station
Project Number: 6030-21000-007-037-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2023
End Date: Jul 31, 2025
Objective:
It is crucial to determine the optimum maturity of sugarcane in order to harvest the cane at the appropriate age. Harvesting under-aged or over-aged cane leads to losses in cane sugar yield, problems in sugar recovery, and poor juice quality. Immature sugarcane has a high content of reducing sugars and color precursor compounds, producing juice with a darker color. Peak maturation is defined by sucrose accumulation with reductions in the content of reducing sugars (primarily glucose and fructose). Hence, it is imperative to know the optimum time to harvest sugarcane.
The maturity of sugarcane is currently determined by visually inspecting the field and measuring the Brix using a hand refractometer on randomly selected plants. The indicators of optimum maturity highly depend on cultivars and the local environment. This method is complex, time and labor-intensive, incurs destructive sample collection, and lacks real-time information on sucrose levels. Although refractometry provides real-time information at a lower cost, the process relies on destructive juice sampling from the plant. The aforementioned processes involve discrete measurements, lack of automation, and remote monitoring, and hence are not robust, and efficient for sampling a large number of plants. Plants are selected randomly in each block for sample collection and the average sucrose content per block is assessed. As a result, the current technology makes sugar monitoring at the plant scale impossible due to the lack of automation and scalability. It is also documented that the selection process in the sugarcane breeding program has faced many challenges in terms of labor, time, and accurate measurements of sucrose content in the progeny. The real-time sensor technology may alleviate this problem by measuring the sucrose content continuously. Thus, breeders could select high sucrose clones with high confidence, which will reduce the field sampling work. Currently, no commercial in situ technology is available to provide real-time and continuous monitoring of sugar content in the sugarcane stalk. The proposed sensor suite is expected to have competitive advantages over the existing methods by providing continuous in situ monitoring capabilities, detection of the spatial and temporal distribution of sugar content directly in the cane stalk, wireless data transfer capability, and low-cost solution while incurring minimal damage to the plant and avoiding the destructive sample collection procedure.
The objective of this proposal is to develop and deploy novel integrated crop-wearable sensors to determine the optimum maturity in sugarcane plants.
1. Developing a low-cost, crop-wearable, wireless sensor for in situ and real-time detection and monitoring of sugar content (i.e., sucrose) directly in the sugarcane stalk.
2. Investigating the impact of real-time and continuous measurements on sugarcane yield and juice quality.
Approach:
The total project will be executed following three phases.
1) A sensor for measuring sucrose will consist of an array of protruded pyramid-shaped microneedles. The entire structure will be made cost-effectively with a 3D printer. The microneedles will penetrate the vascular bundle of the stalk to sample the sap. The sensing mechanism relies on electrochemistry wherein redox reactions of the target molecule on the chemically functionalized electrodes will be translated to current flow proportional to the concentration of the target analyte and be recorded and analyzed by the onboard data acquisition and processing unit to extract the sucrose levels from the previously stored calibration plots. The size of the sensor will be optimized. The sensor will be calibrated with simulated sap solutions spiked with different concentrations of sucrose. Sensor data will be validated with liquid chromatography-mass spectrometry tests conducted on the sap. The sensitivity, selectivity, limit-of-detection, repeatability, reversibility, and temporal stability (through temperature, moisture, and humidity cycling) will be assessed. If needed sensor will be optimized for the nano-coatings for tuning these performance metrics. The data acquisition and processing unit will integrate a potentiostat composed of analog circuits and a microcontroller with an in-built analog-to-digital converter, data analytics for calibration, and a LoRa (short for Long Range) gateway for wireless data transmission so that the time-series levels of sucrose can be wirelessly monitored and stored in the cloud server. The users will be notified remotely. The estimated timeline for this work will be around six months after hiring all the necessary manpower.
2) Once the sensor is characterized and validated in the cooperator’s lab, the sensors will be transferred to the USDA ARS sugarcane research unit both in Houma, LA, and Canal Point, FL for further validation using actual sugarcane juice. First, the sensor performance will be evaluated and compared using both the sensor and the traditional method to measure the sucrose content in the sugarcane sugar juice. A total of 10 sensors will be needed per location and reuse them with several samples for this experiment. Upon getting satisfactory results a small-scale field test will be conducted using 15 sensors and three varieties (5 sensors per variety without rep). The estimated timeline for this phase will be six months.
3) If the above evaluation gives us promising results, a large-scale replicated field trial will be conducted in both locations. Two varieties (one each with high and low sucrose content) and three replications field trials will be conducted for one year. Ten sensors will be attached to 10 stalks of each plot for a longer period for getting real-time sucrose data from the field. Simultaneously, data will be collected using a handheld refractometer and/or NIR. At the end of the season, sugar data will also be taken using NIR in the lab. Thus, a total of 120 (60 per location) sensors will be needed including 50 sensors from phase two. The final phase will take approximately 12 months.
