Today, efficient and cost-effective sensors and high-performance computing technologies are seeking to transform traditional experience-based agriculture into a more efficient smart agriculture. IoT (Internet of Things) technology with low cost, simple deployment, and high communication efficiency creates a great opportunity to collect a large amount of data on different spatial and temporal scales in cultivation and breeding experiments and agricultural production. Meanwhile, supervised machine learning methods (e.g. k nearest neighbors, Bayes' theorem, decision tree, support vector machine, random forest, neural network, convolutional neural network, recurrent neural network, long short-term memory network, gated recurrent unit network), unsupervised machine learning methods (e.g. association rules, k-means, density-based spatial clustering of applications with noise, hierarchical clustering, deep belief networks, deep Boltzmann machine, auto-encoder, denoising auto-encoder, etc.) have been applied to smart agriculture. For instance, machine learning methods have been used to analyze the images of crop disease leaves to recognize the disease type. Furthermore, time-series methods have been proposed to retrieve the time-series features of agricultural big data for prediction purposes. Therefore, for sustainable and profitable agriculture, off-line and real-time agricultural data analysis, heterogeneous data assimilation, and providing automatic and operable information are very important. Therefore, applying machine learning and artificial intelligence methods to this important social demand can be regarded as a revolutionary extension of the agricultural industry.
While the area of machine learning methods for smart agriculture is a rapidly expanding field of scientific research, several open research questions still need to be discussed and studied. For instance, using and improving machine learning methods for crop disease recognition, pest detection, plant species recognition, crop production prediction, precise fertilization, smart agricultural IoT, food material supply-chain security tracing, crop security, and other important issues in smart agriculture.
This Research Topic will introduce the new achievements with machine learning and artificial intelligence algorithms, experimental technology, software, pipeline model, and intelligent agriculture application. Original research and review articles are welcome.
Potential topics include but are not limited to the following:
• Internet of Things and its Application in Smart Agriculture
• Plant Disease Recognition and Prediction in Smart Agriculture
• Artificial Intelligence in Crop Yield Prediction in Smart Agriculture
• Machine Learning Methods for Plant Species Classification
• Block-chain based Methods for Security in Agricultural
• Deep Learning-based Methods for Plant Disease Recognition and Prediction
• New optimization methods for AI Algorithms in Smart Agriculture
• Robotics and Mechatronics in Smart Agriculture
• Agricultural Digitalization, Digital Twins in Smart Agriculture
• Big Data Technology in Smart Agriculture
• Food Material Supply-chain Security Tracing in Smart Agriculture
• Crop Security in Smart Agriculture
Today, efficient and cost-effective sensors and high-performance computing technologies are seeking to transform traditional experience-based agriculture into a more efficient smart agriculture. IoT (Internet of Things) technology with low cost, simple deployment, and high communication efficiency creates a great opportunity to collect a large amount of data on different spatial and temporal scales in cultivation and breeding experiments and agricultural production. Meanwhile, supervised machine learning methods (e.g. k nearest neighbors, Bayes' theorem, decision tree, support vector machine, random forest, neural network, convolutional neural network, recurrent neural network, long short-term memory network, gated recurrent unit network), unsupervised machine learning methods (e.g. association rules, k-means, density-based spatial clustering of applications with noise, hierarchical clustering, deep belief networks, deep Boltzmann machine, auto-encoder, denoising auto-encoder, etc.) have been applied to smart agriculture. For instance, machine learning methods have been used to analyze the images of crop disease leaves to recognize the disease type. Furthermore, time-series methods have been proposed to retrieve the time-series features of agricultural big data for prediction purposes. Therefore, for sustainable and profitable agriculture, off-line and real-time agricultural data analysis, heterogeneous data assimilation, and providing automatic and operable information are very important. Therefore, applying machine learning and artificial intelligence methods to this important social demand can be regarded as a revolutionary extension of the agricultural industry.
While the area of machine learning methods for smart agriculture is a rapidly expanding field of scientific research, several open research questions still need to be discussed and studied. For instance, using and improving machine learning methods for crop disease recognition, pest detection, plant species recognition, crop production prediction, precise fertilization, smart agricultural IoT, food material supply-chain security tracing, crop security, and other important issues in smart agriculture.
This Research Topic will introduce the new achievements with machine learning and artificial intelligence algorithms, experimental technology, software, pipeline model, and intelligent agriculture application. Original research and review articles are welcome.
Potential topics include but are not limited to the following:
• Internet of Things and its Application in Smart Agriculture
• Plant Disease Recognition and Prediction in Smart Agriculture
• Artificial Intelligence in Crop Yield Prediction in Smart Agriculture
• Machine Learning Methods for Plant Species Classification
• Block-chain based Methods for Security in Agricultural
• Deep Learning-based Methods for Plant Disease Recognition and Prediction
• New optimization methods for AI Algorithms in Smart Agriculture
• Robotics and Mechatronics in Smart Agriculture
• Agricultural Digitalization, Digital Twins in Smart Agriculture
• Big Data Technology in Smart Agriculture
• Food Material Supply-chain Security Tracing in Smart Agriculture
• Crop Security in Smart Agriculture
