Evaluating rapid plant tissue analysis method for nitrogen diagnostics in corn (Zea mays L.) production

Background Accurate assessment of plant nitrogen status is essential for optimizing fertilizer inputs, increasing productivity, and ensuring environmental quality. This study compared three nitrogen status assessment in corn ( Zea mays L.): visual assessment method, a novel real-time nutrient estimation using the Picketa LENS™ system, and the conventional laboratory tissue analysis as a reference method. To evaluate the accuracy of the Picketa LENS™ system, a field experiment with four nitrogen treatments (0% nitrogen (control), 80% nitrogen, 100% nitrogen, and 100% nitrogen + stabilizer) and four replications was conducted in York County, Nebraska. Results Visual assessment detected treatment differences, with the 0% nitrogen plots showing severe chlorosis and a progressive decline (63.4% reduction) in healthy leaves below the ear over five weeks, whereas 100% nitrogen maintained consistently higher healthy leaf counts (only a 7.4% reduction). However, visual assessment showed limited ability to distinguish between 80% nitrogen and 100% nitrogen + stabilizer treatments until weeks four and five. Both quantitative methods did not detect treatment differences due to the sampled leaf position. The Picketa system (2024 corn model) reported higher absolute nitrogen concentrations (approximately 4.4–4.6%) than laboratory analysis (2.9–3.2%) across all treatments and did not detect significant treatment effects. Conventional laboratory analysis detected only a modest increase in the 100% nitrogen treatment compared with the 0% control. For macronutrients, the Picketa system measured concentrations higher than conventional tissue sampling for phosphorus, potassium, and calcium, with potassium showing approximately 40% higher values and calcium showing 40–50% higher values, while magnesium and sulfur showed close agreement between methods. Micronutrient analysis revealed that the Picketa system consistently reported higher concentrations than conventional tissue sampling for iron (45% higher), manganese (approximately 4-fold higher), copper (90% higher), and zinc (33% higher), but reported significantly lower boron concentrations (67% lower). Despite these absolute value differences, both methods demonstrated similar patterns of detection across treatments. Conclusions Visual assessment effectively detected treatment differences, while the Picketa System and the conventional method did not, but maintained similar patterns. These findings highlight the promise of Picketa LENS and the importance of matching sample positions and timing to diagnostic objectives. Integrating real-time sensing with conventional methods can improve nitrogen management decisions.