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[HTML][HTML] Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies
This paper presents a comprehensive review of emerging technologies for the internet of
things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and …
things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and …
Classification and detection of insects from field images using deep learning for smart pest management: A systematic review
W Li, T Zheng, Z Yang, M Li, C Sun, X Yang - Ecological Informatics, 2021 - Elsevier
Insect pest is one of the main causes affecting agricultural crop yield and quality all over the
world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction …
world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction …
[HTML][HTML] Deep learning based detector YOLOv5 for identifying insect pests
Insect pests are a major element influencing agricultural production. According to the Food
and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each …
and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each …
Transfer learning-based deep ensemble neural network for plant leaf disease detection
S Vallabhajosyula, V Sistla, VKK Kolli - Journal of Plant Diseases and …, 2022 - Springer
Plant diseases are a vital risk to crop yield and early detection of plant diseases remains a
complex task for the farmers due to the similar appearance in color, shape, and texture. In …
complex task for the farmers due to the similar appearance in color, shape, and texture. In …
[HTML][HTML] A new mobile application of agricultural pests recognition using deep learning in cloud computing system
ME Karar, F Alsunaydi, S Albusaymi… - Alexandria Engineering …, 2021 - Elsevier
Agricultural pests cause between 20 and 40 percent loss of global crop production every
year as reported by the Food and Agriculture Organization (FAO). Therefore, smart …
year as reported by the Food and Agriculture Organization (FAO). Therefore, smart …
Advances in deep concealed scene understanding
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …
objects exhibiting camouflage. The current boom in terms of techniques and applications …
Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current
context of climate change, which is causing the destabilization of agricultural practices and …
context of climate change, which is causing the destabilization of agricultural practices and …
A systematic review on automatic insect detection using deep learning
Globally, insect pests are the primary reason for reduced crop yield and quality. Although
pesticides are commonly used to control and eliminate these pests, they can have adverse …
pesticides are commonly used to control and eliminate these pests, they can have adverse …
Automated pest detection with DNN on the edge for precision agriculture
Artificial intelligence has smoothly penetrated several economic activities, especially
monitoring and control applications, including the agriculture sector. However, research …
monitoring and control applications, including the agriculture sector. However, research …
[HTML][HTML] Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions
Abstract The Artificial Intelligence of Things (AIoT), a combination of the Internet of Things
(IoT) and Artificial Intelligence (AI), plays an increasingly important role in smart agriculture …
(IoT) and Artificial Intelligence (AI), plays an increasingly important role in smart agriculture …