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Marios Mamalis, Evangelos Kalampokis, Ilias Kalfas and Konstantinos Tarabanis
The verticillium fungus has become a widespread threat to olive fields around the world in recent years. The accurate and early detection of the disease at scale could support solving the problem. In this paper, we use the YOLO version 5 model to detect ...
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Juan Contreras-Castillo, Juan Antonio Guerrero-Ibañez, Pedro C. Santana-Mancilla and Luis Anido-Rifón
The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data coll...
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Jonathan Vance, Khaled Rasheed, Ali Missaoui and Frederick W. Maier
Alfalfa is critical to global food security, and its data is abundant in the U.S. nationally, but often scarce locally, limiting the potential performance of machine learning (ML) models in predicting alfalfa biomass yields. Training ML models on local-o...
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Muhammad Akhtar, Iqbal Murtza, Muhammad Adnan and Ayesha Saadia
Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and size o...
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Mino Sportelli, Orly Enrique Apolo-Apolo, Marco Fontanelli, Christian Frasconi, Michele Raffaelli, Andrea Peruzzi and Manuel Perez-Ruiz
The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which ...
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