… utilizing novel optimized ML and deep learning (DL) techniques that will … learning, deep
learning in the field of agriculture, mainly rice disease, and other areas where machine learning …

… in some large-scale problems, CNN can learn complex problems particularly fast because
of … In the current paper, a survey of CNN-based research efforts applied in the agricultural …

… And the deep learning mode can be launched in this simplified architecture. The … of precise
agriculture. In this study, a shallow CNN architecture is proposed to construct a deep learning …

… CNN worked for optical character recognition (OCR). Chen has developed networks with an
accuracy of about 98% (Chen et al., 2015). In this paper a CNN … or synthetical agricultural …

… Given the rapid development in label-efficient learning, our work attempts to comprehensively
review the state-of-the-art algorithms with focuses on agricultural applications, covering …

… , machine learning offers a solution to problems of agricultural production and assures food
security. Deep learning or deep structured learning is a subset of machine learning where …

… This work paid special attention to the application of deep learning in precision agriculture.
Specifically, our main focus was on three kinds of deep learning methods, namely CNN-SL, …

… learning techniques explored in [16], etc. The performance of deep learning methods for
agriculture … related to the use of AI and IoT tools in agriculture and certain real-life situations. The …

… productivity in agriculture with a developed new deeplearning-based … In addition, the
designed CNN architecture was used instead … Instead of semi-trained CNN models, a new CNN …

… deep learning models for the accurate agriculture pattern segmentation from aerial images.
… agriculture patterns classes We collected the Agriculture Vision dataset from the Agriculture-…