In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards

Smart agriculture utilizes sensors and data analytics to automate management, ultimately enhancing crop yields and quality. Sensors play a pivotal role in realizing the potential of smart agriculture. However, many studies rely on environment-aware monitoring, which may not be comprehensive enough, as different crops respond uniquely to similar environments. This study presents a novel approach for assessing crop growth and health status during agricultural production. To achieve this, we designed five agricultural sensor systems, consisting of four common sensor types with varying shapes (lamellar, wrap-around, ring, and needle), alongside one modified array sensor system. Validation was conducted by simultaneously measuring multiple fruit trees. The findings revealed that the needle system and the array system were particularly effective at detecting fruit growth. Notably, only the array system demonstrated the capability to monitor fruit health status. Additionally, we introduced fruit quality parameters (QP) as a means to visualize their health status. Statistical analyses demonstrated a strong correlation (r > 0.85) and reliable prediction (R2>0.80) between both individual and tissue impedance of fruits and their chemical indicators. In particular, the impedance variation of the fruit can effectively reflect changes in its internal structure and moisture content. By analyzing the impedance data at different frequencies, the growth condition of the mango fruit can be accurately assessed. Furthermore, Granger's causality test showed that changes in fruit tissue impedance statistically led to changes in individual impedance. The sensor system developed in this study is a real-time and in-situ crop detection tool that promotes smart agriculture.

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