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Shadi Atalla, Saed Tarapiah, Amjad Gawanmeh, Mohammad Daradkeh, Husameldin Mukhtar, Yassine Himeur, Wathiq Mansoor, Kamarul Faizal Bin Hashim and Motaz Daadoo
The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifica...
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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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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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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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