Proceedings :: International Society of Precision Agriculture

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Lo Celso, I

Kusnierek, K

Mintesinot, S.M

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Authors

Dong, L

Miao, Y

Wang, X

Berry, P

Hatley, D

Kusnierek, K

Mintesinot, S.M

Santana, C.C

Queiroz, D

Pereira, M.H

Coelho, A.L

Santana, C.C

Mintesinot, S.M

Queiroz, D

Santos, F.S

Coelho, A.L

Lo Celso, I

Ciancio, N

Scaramuzza, F.M

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Precision Crop Protection, Pest, and Plant Health

Type

Oral

Poster

Year

2026

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Filter results4 paper(s) found.
1. Early Forecasting of Maize Lodging Risk Through Multi-period and Multi-source Data Integration Lodging is a critical constraint on global maize (Zea mays L.) productivity, primarily through detrimental effects on both grain yield and quality. However, reliable methods to predict maize lodging risk early in the growing season are lacking, which hinders timely implementation of effective agronomic management interventions to increase crop lodging resistance and reduce corresponding yield losses. This work aimed to develop a feasible early season maize lodging risk prediction method... L. Dong, Y. Miao, X. Wang, P. Berry, D. Hatley, K. Kusnierek Download paper
2. Hyperspectral Estimation of Corn Leaf Chlorophyll Index Using a Wavelet-Guided Spectral Index Optimization Framework The corn leaf chlorophyll index (LCI) is a proxy indicator of plant photosynthetic capacity and nitrogen nutritional status, thereby supporting precision nutrient management and data-driven agronomic decision-making. Conventional methods, such as handheld chlorophyll meters, provide accurate leaf-level measurements but are labor-intensive, time-consuming, and inherently limited in their ability to represent field-scale spatial variability due to their point-based sampling nature. Hyperspectral... S.M. Mintesinot, C.C. Santana, D. Queiroz, M.H. Pereira, A.L. Coelho Download paper
3. Estimation of Leaf Chlorophyll Index in Corn Using Smartphone Images and Machine Learning Accurate estimation of the Leaf Chlorophyll Index (LCI) in corn is fundamental for nitrogen management in precision agriculture, as nitrogen availability directly affects chlorophyll production and photosynthetic capacity. Conventional field assessment techniques are time-consuming and labor-intensive, while smartphone use provides a practical and low-cost alternative for obtaining high-resolution data in near-real-time. The objective of this study was to develop and validate a non-destructive... C.C. Santana, S.M. Mintesinot, D. Queiroz, F.S. Santos, A.L. Coelho Download paper
4. From Area-wide Management to Site-specific and Individual Plant Management: the Evolution of Weed Control in Argentina Selective herbicide spraying represents one of the most significant innovations in the evolution of Precision Agriculture, enabling a shift from uniform broadcast applications toward spatially explicit and plant-by-plant management models. In a context characterized by the increasing prevalence of herbicide-resistant weed biotypes, rising input costs, and growing environmental and regulatory pressure, these technologies are becoming strategic tools to enhance input-use efficiency and improve the... I. Lo Celso, N. Ciancio, F.M. Scaramuzza

Filter results4 paper(s) found.

1. Early Forecasting of Maize Lodging Risk Through Multi-period and Multi-source Data Integration

Lodging is a critical constraint on global maize (Zea mays L.) productivity, primarily through detrimental effects on both grain yield and quality. However, reliable methods to predict maize lodging risk early in the growing season are lacking, which hinders timely implementation of effective agronomic management interventions to increase crop lodging resistance and reduce corresponding yield losses. This work aimed to develop a feasible early season maize lodging risk prediction method... L. Dong, Y. Miao, X. Wang, P. Berry, D. Hatley, K. Kusnierek

Download paper

2. Hyperspectral Estimation of Corn Leaf Chlorophyll Index Using a Wavelet-Guided Spectral Index Optimization Framework

The corn leaf chlorophyll index (LCI) is a proxy indicator of plant photosynthetic capacity and nitrogen nutritional status, thereby supporting precision nutrient management and data-driven agronomic decision-making. Conventional methods, such as handheld chlorophyll meters, provide accurate leaf-level measurements but are labor-intensive, time-consuming, and inherently limited in their ability to represent field-scale spatial variability due to their point-based sampling nature. Hyperspectral... S.M. Mintesinot, C.C. Santana, D. Queiroz, M.H. Pereira, A.L. Coelho

Download paper

3. Estimation of Leaf Chlorophyll Index in Corn Using Smartphone Images and Machine Learning

Accurate estimation of the Leaf Chlorophyll Index (LCI) in corn is fundamental for nitrogen management in precision agriculture, as nitrogen availability directly affects chlorophyll production and photosynthetic capacity. Conventional field assessment techniques are time-consuming and labor-intensive, while smartphone use provides a practical and low-cost alternative for obtaining high-resolution data in near-real-time. The objective of this study was to develop and validate a non-destructive... C.C. Santana, S.M. Mintesinot, D. Queiroz, F.S. Santos, A.L. Coelho

Download paper

4. From Area-wide Management to Site-specific and Individual Plant Management: the Evolution of Weed Control in Argentina

Selective herbicide spraying represents one of the most significant innovations in the evolution of Precision Agriculture, enabling a shift from uniform broadcast applications toward spatially explicit and plant-by-plant management models. In a context characterized by the increasing prevalence of herbicide-resistant weed biotypes, rising input costs, and growing environmental and regulatory pressure, these technologies are becoming strategic tools to enhance input-use efficiency and improve the... I. Lo Celso, N. Ciancio, F.M. Scaramuzza