Proceedings :: International Society of Precision Agriculture

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Felipe, J.C

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Authors

Ferreira , J

Françani, A.O

Ferreira, E.

Jorge, L.A

Felipe, J.C

Zhao, L

Martins, T.M

Tetila, E.C

Barbedo, J.G

Felipe, J.C

Zhao, L

Lima, M

Felipe, J.C

Ferreira, E.J

Jorge, L.A

Zhao, L

Françani, A.O

Ferreira , J

Zhao, L

Jorge, L.A

de Oliveira, K.M

Felipe, J.C

Topics

Remote and Proximal Sensing of Soils and Crops

UAV-Based Scouting, Imaging, and Targeted Applications

Precision Crop Protection, Pest, and Plant Health

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Poster

Oral

Year

2026

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Filter results4 paper(s) found.
1. Stability-driven Framework for Robust Plant Spectral Signature Identification Accurate identification of agricultural crops based on spectral signatures remains a critical challenge for large-scale phytosanitary monitoring. This study proposes a stability-based structure for the robust identification of plant spectral signatures, applied to the discrimination of soybean (Glycine max) from maize (Zea mays) and cotton (Gossypium hirsutum) under biotic stress caused by the pest Spodoptera frugiperda and stink bugs. The proposed method follows a flow of proposed steps that... J. Ferreira , A.O. Françani, E. . Ferreira, L.A. Jorge, J.C. Felipe, L. Zhao Download paper
2. Enhancing Weed Detection in Corn Crops Through Attention-based Models and Curated Datasets Weed infestation is one of the leading causes of global agricultural productivity losses, directly impacting production costs, environmental sustainability, and food security. In precision agriculture, automated weed detection from aerial imagery enables site-specific herbicide application, reducing chemical overuse and environmental impact. Deep learning-based computer vision techniques have been widely adopted for this purpose, with Convolutional Neural Networks (CNNs) historically dominating... T.M. Martins, E.C. Tetila, J.G. Barbedo, J.C. Felipe, L. Zhao Download paper
3. Early Detection of Soybean Pest Infestations Using Leaf-Level Reflectance Spectroradiometry and Machine Learning The agricultural sector plays a central role in sustaining global food production, energy supply, and economic development. However, population growth, climate change, resource scarcity, and increasing sustainability demands have intensified production challenges. Pest and disease outbreaks are major contributors to crop losses worldwide, underscoring the urgent need for reliable methods capable of enabling early detection and timely intervention. In this context, leaf-level spectroradiometry... M. Lima, J.C. Felipe, E.J. Ferreira, L.A. Jorge, L. Zhao Download paper
4. Enhancing Pest Detection Through Spectral Signature Extraction in Hyperspectral Data Detecting insect infestation is essential for effective crop protection, particularly in large-scale systems. Caterpillars and stink bugs induce physiological and structural alterations in plant tissues that can be captured through hyperspectral reflectance sensing, which is a non-destructive technique that measures plant responses across hundreds of wavelengths. However, raw spectral signatures are characterized by high dimensionality, strong inter-band correlation, and they often exhibit baseline... A.O. Françani, J. Ferreira , L. Zhao, L.A. Jorge, K.M. De Oliveira, J.C. Felipe Download paper

Filter results4 paper(s) found.

1. Stability-driven Framework for Robust Plant Spectral Signature Identification

Accurate identification of agricultural crops based on spectral signatures remains a critical challenge for large-scale phytosanitary monitoring. This study proposes a stability-based structure for the robust identification of plant spectral signatures, applied to the discrimination of soybean (Glycine max) from maize (Zea mays) and cotton (Gossypium hirsutum) under biotic stress caused by the pest Spodoptera frugiperda and stink bugs. The proposed method follows a flow of proposed steps that... J. Ferreira , A.O. Françani, E. . Ferreira, L.A. Jorge, J.C. Felipe, L. Zhao

Download paper

2. Enhancing Weed Detection in Corn Crops Through Attention-based Models and Curated Datasets

Weed infestation is one of the leading causes of global agricultural productivity losses, directly impacting production costs, environmental sustainability, and food security. In precision agriculture, automated weed detection from aerial imagery enables site-specific herbicide application, reducing chemical overuse and environmental impact. Deep learning-based computer vision techniques have been widely adopted for this purpose, with Convolutional Neural Networks (CNNs) historically dominating... T.M. Martins, E.C. Tetila, J.G. Barbedo, J.C. Felipe, L. Zhao

Download paper

3. Early Detection of Soybean Pest Infestations Using Leaf-Level Reflectance Spectroradiometry and Machine Learning

The agricultural sector plays a central role in sustaining global food production, energy supply, and economic development. However, population growth, climate change, resource scarcity, and increasing sustainability demands have intensified production challenges. Pest and disease outbreaks are major contributors to crop losses worldwide, underscoring the urgent need for reliable methods capable of enabling early detection and timely intervention. In this context, leaf-level spectroradiometry... M. Lima, J.C. Felipe, E.J. Ferreira, L.A. Jorge, L. Zhao

Download paper

4. Enhancing Pest Detection Through Spectral Signature Extraction in Hyperspectral Data

Detecting insect infestation is essential for effective crop protection, particularly in large-scale systems. Caterpillars and stink bugs induce physiological and structural alterations in plant tissues that can be captured through hyperspectral reflectance sensing, which is a non-destructive technique that measures plant responses across hundreds of wavelengths. However, raw spectral signatures are characterized by high dimensionality, strong inter-band correlation, and they often exhibit baseline... A.O. Françani, J. Ferreira , L. Zhao, L.A. Jorge, K.M. De Oliveira, J.C. Felipe

Download paper