Proceedings

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Chiang, R.C
Conley, S
Celades, J.A
Chen, N
Castro, S.G
Company, J
Chen, T
Clark, N
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Authors
Arno, J
DEL MORAL, I
Escolà, A
Company, J
MARTÍNEZ-CASASNOVAS, J.A
MASIP, J
SANZ, R
ROSELL, J.R
Kolln, O.T
Sanches, G.M
Rossi Neto, J
Castro, S.G
Mariano, E
Otto, R
Inamasu, R
Magalhães, P.S
Braunbeck, O.A
Franco, H.C
Zhang, Y
Chen, T
Castro, S.G
Sanches, G.M
Cardoso, G.M
Silva, A.E
Franco, H.C
Magalhães, P.S
Herrmann, I
Vosberg, S
Ravindran, P
Singh, A
Townsend, P
Conley, S
Kantipudi, K
Lai, C
Min, C
Chiang, R.C
Celades, J.A
Caicedo, J.H
García, C.E
Mora, H
Grijalva Teran, I.A
Spiesman, B
Clark, N
McCornack, B
Chen, N
Topics
Proximal Sensing in Precision Agriculture
Precision Nutrient Management
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change, Standards)
Precision Nutrient Management
Precision Agriculture and Global Food Security
Big Data, Data Mining and Deep Learning
Profitability and Success Stories in Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2014
2016
2018
2024
2025
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Filter results9 paper(s) found.

1. Mapping the Leaf Area Index In Vineyard Using a Ground-Based LIDAR Scanner

The leaf area index (LAI) is defined as the one-sided leaf area per unit ground area and is probably the most widely used index to characterize grapevine vigour. However, direct LAI measurement requires the use of destructive leaves sampling methods which are costly and time-consuming and so are other indirect methods. Faced with these techniques, vineyard leaf area can be indirectly estimated using ground-based LIDAR sensors that scan the vines and get information about the geometry and/or structure... J. Arno, I. Del moral, A. Escolà, J. Company, J.A. MartÍnez-casasnovas, J. Masip, R. Sanz, J.R. Rosell

2. Application of Semantic Sensor Web in Agriculture

      In July 2013, heavy rainstorms across the Midwestern region of the US caused many rivers to breach their banks. Residents of Valley Park, a small town along the Meramec River, Missouri, had to decide whether to rely on a newly constructed levee or abandon their homes for higher ground. Although the levee held, many chose the latter option and fled their homes; it was a chaotic situation that might have been avoided through access to better situational knowledge... Y. Zhang, T. Chen

3. Optical Sensors To Predict Nitrogen Demand By Sugarcane

The low effectiveness of nitrogen (N) from fertilizer is a substantial concern in worldwide which has been threatening the sustainability of sugarcane production. The increment of nitrogen use efficiency (NUE) by sugarcane genotypes associated to the best practices of fertilizer management and nutritional diagnosis methods have higher potential to reduce environment impacts of nitrogen fertilization. Due to the difficult to determine N status in soil test as well as there is not... O.T. Kolln, G.M. Sanches, J. Rossi neto, S.G. Castro, E. Mariano, R. Otto, R. Inamasu, P.S. Magalhães, O.A. Braunbeck, H.C. Franco

4. Use of Crop Canopy Reflectance Sensor in Management of Nitrogen Fertilization in Sugarcane in Brazil

Given the difficulty to determine N status in soil testing and lack of crop parameters to recommend N for sugarcane in Brazil raise the necessity of identify new methods to find crop requirement to improve the N use efficiency. Crop canopy sensor, such as those used to measure indirectly chlorophyll content as N status indicator, can be used to monitor crop nutritional demand. The objective of this experiment was to assess the nutritional status of the sugarcane fertilized with different nitrogen... S.G. Castro, G.M. Sanches, G.M. Cardoso, A.E. Silva, H.C. Franco, P.S. Magalhães

5. Exploring Tractor Mounted Hyperspectral System Ability to Detect Sudden Death Syndrome Infection and Assess Yield in Soybean

Pre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measurements... I. Herrmann, S. Vosberg, P. Ravindran, A. Singh, P. Townsend, S. Conley

6. Weed Detection Among Crops by Convolutional Neural Networks with Sliding Windows

One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural... K. Kantipudi, C. Lai, C. Min, R.C. Chiang

7. Toward a Precision Agricultural Implementation for Sugar Cane Plantations in Southwestern Region of Colombia, South America

The Colombian Sugar Cane Research Center, CENICAÑA, has initiated an ambitious project for the implementation of Precision Agriculture (PA) technologies in the Cauca river valley region, where one of its main objectives is to have the ability to collect large volumes of geospatial data. The main sugarcane growers in the country perform their work in the selected work area, which covers an area of ​​approximately 242,000 ha, characterized by diverse topographic and edaphic conditions.... J.A. Celades, J.H. Caicedo, C.E. García, H. Mora

8. Detection of Sorghum Aphids with Advanced Machine Vision

Sorghum aphid, Melanaphis sorghi (Theobald), became a significant pest concern due to the significant yield losses caused in the sorghum production region. Different management practices, including monitoring and applying insecticides, have been used to manage this invasive pest in sorghum. The most common management strategy consists of visual assessments of aphids on sorghum leaves to determine an economic threshold level to spray. However, because of their rapid reproduction,... I.A. Grijalva teran, B. Spiesman, N. Clark, B. Mccornack

9. Embodied Agentic Artificial Intelligence for Precision Agriculture: Cross-domain Experience from Multimodal Generative AI

My team develops inclusive, responsible, and multimodal AI technology across education, healthcare, and digital services grounded in our research in embodied agentic intelligence and large language models. I will share deployed examples from these domains and draw parallels to agriculture, where similar technical challenges persist, ranging from multimodal fusion for contextual reasoning, explainable AI for actionable insights, and data-efficient learning for adaptation and localization. While... N. Chen