Popescu, Dan Dinca, Alexandru Ichim, Loretta Angelescu, Nicoleta
Published in
Frontiers in Plant Science
Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, res...
Chen, Haitao Han, Yujing Liu, Yongchang Liu, Dongyang Jiang, Lianqiang Huang, Kun Wang, Hongtao Guo, Leifeng Wang, Xinwei Wang, Jie
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Published in
Frontiers in Plant Science
Tobacco Mosaic Virus (TMV) and Potato Virus Y (PVY) pose significant threats to crop production. Non-destructive and accurate surveillance is crucial to effective disease control. In this study, we propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infec...
Kilmanun, Juliana Carolina Prahardini, Paulina Evy Retnaning Anita, Sofia Sutoto, Agung Prabowo, Jonathan Saptana, Wahyono, Eko Putri, Rumanintya Lisaria
Published in
BIO Web of Conferences
As a developing nation, Indonesia’s economy relies heavily on agriculture. Despite being the largest food producer in Southeast Asia, the Indonesian government is primarily concerned with ensuring domestic food security. Consequently, the Indonesian government is actively working to enhance agricultural infrastructure and capabilities. However, thi...
Molina-Rotger, Miguel Morán, Alejandro Miranda, Miguel Angel Alorda-Ladaria, Bartomeu
Published in
Frontiers in Plant Science
Introduction Intelligent monitoring systems must be put in place to practice precision agriculture. In this context, computer vision and artificial intelligence techniques can be applied to monitor and prevent pests, such as that of the olive fly. These techniques are a tool to discover patterns and abnormalities in the data, which helps the early ...
Shi, Yue Han, Liangxiu González-Moreno, Pablo Dancey, Darren Huang, Wenjiang Zhang, Zhiqiang Liu, Yuanyuan Huang, Mengning Miao, Hong Dai, Min
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Published in
Frontiers in Plant Science
Introduction Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models alw...
Marti-Jerez, Karen Català-Forner, Mar Tomàs, Núria Murillo, Gemma Ortiz, Carlos Sánchez-Torres, María José Vitali, Andrea Lopes, Marta S.
Published in
Frontiers in Plant Science
Introduction Rice heavily relies on nitrogen fertilizers, posing environmental, resource, and geopolitical challenges. This study explores sustainable alternatives like animal manure and remote sensing for resource-efficient rice cultivation. It aims to assess the long-term impact of organic fertilization and remote sensing monitoring on agronomic ...
Cudjoe, Daniel K. Virlet, Nicolas Castle, March Riche, Andrew B. Mhada, Manal Waine, Toby W. Mohareb, Fady Hawkesford, Malcolm J.
Published in
Frontiers in Plant Science
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers f...
Abrahams, M. Sibanda, M. Dube, T. Chimonyo, V. G. P. Mabhaudhi, Tafadzwanashe
Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data...
Xu, Mingle Kim, Hyongsuk Yang, Jucheng Fuentes, Alvaro Meng, Yao Yoon, Sook Kim, Taehyun Park, Dong Sun
Published in
Frontiers in Plant Science
Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning–based methods in real-world scenarios is ...
Orka, Nabil Anan Uddin, M. Nazim Toushique, Fardeen Md. Hossain, M. Shahadath
Published in
Frontiers in Plant Science
