Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato

Abstract

Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red–green–blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop’s status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.

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“…Moreover, a high correlation between the bioristor R index and common vegetative indices used in field monitoring was observed, as well as a significant correlation with the crop stress water index [25]. Based on these results, bioristor was proposed as a tool for field phenotyping [25], coupled with a model based on artificial intelligence (AI) to forecast water stress in tomatoes [29]. Overall, bioristor proved to be able to monitor the functional physiology of apples, grapes, and kiwis [30].…”

“…The bioristor sensors was fabricated, installed, and operated following previously reported methods [23,25,29].…”

“…The relative variation in the channel current represents the sensor response (R), which is proportional to the concentration of ions dissolved in the plant sap [23]. Bioristor successfully monitored the changes occurring throughout the entire plant growth period, showing a high durability and accurate measurements both in vitro and in vivo [24,25]. It also demonstrated the ability to detect changes in the plant physiology mechanisms and the occurrence of water stress [25,26].…”

“…Bioristor successfully monitored the changes occurring throughout the entire plant growth period, showing a high durability and accurate measurements both in vitro and in vivo [24,25]. It also demonstrated the ability to detect changes in the plant physiology mechanisms and the occurrence of water stress [25,26]. During a water deficit, a severe change in plant physiological mechanisms occurs, leading to response mechanisms such as reduced transpiration, stomatal closure, reduced photosynthesis, accumulation of ABA, and ion compartmentalization [26,27].…”

“…In the field, bioristor was applied to monitor tomato crops throughout the entire production season, leading to the hypothesis that its adoption as a tool to guide irrigation could have led to 36% water savings [28]. Moreover, a high correlation between the bioristor R index and common vegetative indices used in field monitoring was observed, as well as a significant correlation with the crop stress water index [25]. Based on these results, bioristor was proposed as a tool for field phenotyping [25], coupled with a model based on artificial intelligence (AI) to forecast water stress in tomatoes [29].…”