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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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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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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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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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Muhammad Waqas, Usa Wannasingha Humphries, Angkool Wangwongchai, Porntip Dechpichai and Shakeel Ahmad
Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting...
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