Automation in agriculture by machine and deep learning techniques: A review of recent developments
Recently, agriculture has gained much attention regarding automation by artificial
intelligence techniques and robotic systems. Particularly, with the advancements in machine …
intelligence techniques and robotic systems. Particularly, with the advancements in machine …
Characterising the agriculture 4.0 landscape—emerging trends, challenges and opportunities
Investment in technological research is imperative to stimulate the development of
sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and …
sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and …
A review of deep learning in multiscale agricultural sensing
Population growth, climate change, and the worldwide COVID-19 pandemic are imposing
increasing pressure on global agricultural production. The challenge of increasing crop yield …
increasing pressure on global agricultural production. The challenge of increasing crop yield …
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 …
Deep learning for precision agriculture: A bibliometric analysis
Recent advances in communication technologies with the emergence of connected objects
have changed the agricultural area. In this new digital age, the development of artificial …
have changed the agricultural area. In this new digital age, the development of artificial …
PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection
Plant diseases and pests cause significant losses in agriculture, with economic, ecological
and social implications. Therefore, early detection of plant diseases and pests via automated …
and social implications. Therefore, early detection of plant diseases and pests via automated …
Meta-learning baselines and database for few-shot classification in agriculture
Learning from a few samples to automatically recognize the pests or plants is an attractive
and promising study with a low cost of data to protect the agricultural yield and quality …
and promising study with a low cost of data to protect the agricultural yield and quality …
DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection
Plant-leaf disease detection is one of the key problems of smart agriculture which has a
significant impact on the global economy. To mitigate this, intelligent agricultural solutions …
significant impact on the global economy. To mitigate this, intelligent agricultural solutions …
Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse
Agricultural pest catches on sticky traps can be used for the early detection and identification
of hotspots, as well as for estimating relative abundances of adult pests, occurring in …
of hotspots, as well as for estimating relative abundances of adult pests, occurring in …
Few-shot cotton pest recognition and terminal realization
**njiang is the major cotton-producing area in China, also well known in the world for its
high-quality cotton. The growth and quality of cotton are closely related to the pest attack, but …
high-quality cotton. The growth and quality of cotton are closely related to the pest attack, but …