Proceedings :: International Society of Precision Agriculture

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Peixoto, A.S

Mattupalli, C

Buragiene, S

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Authors

Kriauciuniene, Z

Kazlauskas, M

Romaneckas, K

Buragiene, S

Bručienė, I

Šarauskis, E

Loganathan Girija, D

Usama Bin Sabir, S

Rathore, D

Khot, L.R

Mattupalli, C

Karkee, M

Karasinski, M.A

Bendahan, A.B

Jorge, L.A

Gabriel da Silva Carmo , I.L

Barreto, G.F

Peixoto, A.S

Dantas Oliveira, S.V

Thomé Barbosa, R.N

Schurt, D.A

Topics

Site-Specific Nutrient, Lime and Seed Management

Agricultural Robotics, Automation, and Mechanization

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Oral

Poster

Year

2026

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Filter results3 paper(s) found.
1. The Agronomic and Bioeconomic Aspects of Site-Specific Seeding Rates and Depths for Winter Wheat in Lithuania Precision seeding is one of the most important agrotechnological solutions for smart agriculture. It exploits the variability of soil properties in the field to increase the agronomic and economic efficiency of crops. This study investigated the impact of site-specific seeding (SSS) on the yield and productivity parameters of winter wheat in Lithuania, as well as its economic benefits, compared with conventional uniform rate seeding (URS). Experiments were conducted in a field divided into five... Z. Kriauciuniene, M. Kazlauskas, K. Romaneckas, S. Buragiene, I. Bručienė, E. Šarauskis Download paper
2. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee
3. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean pod Accurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aimed...

Filter results3 paper(s) found.

1. The Agronomic and Bioeconomic Aspects of Site-Specific Seeding Rates and Depths for Winter Wheat in Lithuania

Precision seeding is one of the most important agrotechnological solutions for smart agriculture. It exploits the variability of soil properties in the field to increase the agronomic and economic efficiency of crops. This study investigated the impact of site-specific seeding (SSS) on the yield and productivity parameters of winter wheat in Lithuania, as well as its economic benefits, compared with conventional uniform rate seeding (URS). Experiments were conducted in a field divided into five... Z. Kriauciuniene, M. Kazlauskas, K. Romaneckas, S. Buragiene, I. Bručienė, E. Šarauskis

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2. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers

High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee

3. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean pod

Accurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aimed...