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Fire detection based on computer vision technology can avoid many flaws in conventional methods. However, existing methods fail to achieve a good trade-off in accuracy, model size, speed, and cost. This paper presents a high-performance forest fire recognition algorithm to solve the current problems in forest fire monitoring. Firstly, visual saliency areas in motion images are extracted to improve detection efficiency. Secondly, transfer learning techniques are employed to improve the generalization performance of the constructed deep learning classification model. Finally, fire detection is realized based on C++ deployment algorithms Compared with the existing forest fire detection methods, the proposed method has higher classification accuracy and speed, with a more comprehensive application range and lower cost. The performance of our method can meet the accuracy and speed requirements of real-time fire detection, and it can be deployed and practiced on multiple platforms. [ABSTRACT FROM AUTHOR]

Most leaf chlorophyll predictions based on digital image analyzes are modeled by manual extraction features and traditional machine learning methods. In this study, a series of image preprocessing operations, such as image threshold segmentation, noise processing, and background separation, were performed based on digital image processing technology to remove the background and noise interference. The intrinsic features of the leaf RGB image were automatically learned through a stacked sparse autoencoder (SSAE) network to obtain concise data features. Finally, a prediction model between the RGB image features of a leaf and its SPAD value (arbitrary units) was established to predict the chlorophyll content in the plant leaf. The results show that the accuracy and automation of the detection of chlorophyll content of the deep neural network in this study are higher than those of traditional machine learning methods. [ABSTRACT FROM AUTHOR]

• Hand-designed features are used to reduce computation load and false alarm rate. • We proposed a small and efficient CNN model for fast and intelligent smoke detection. • The method has excellent performance and generalization ability in new environments. • The detection time of a single frame is only 36.45 ms with a CPU and multithreading. In this paper, a fast and accurate video smoke detection algorithm is proposed to extract a powerful feature representation of forest fire. Because of the irregular shape, the variation of colors and hard-to-depict texture of smoke, it is incredibly challenging to detect smoke quickly and accurately in a complex environment. We proposes a smoke detection algorithm that combines manual features and deep learning features, an approach that differs from those of existing detection methods. First, a manual design algorithm is used to extract the suspected smoke area, which is then employed as input to the improved very small deep neural network SqueezeNet model to achieve smoke detection. Extensive detection experiments are conducted, and the results showed that the proposed method is able to differentiate smoke from other challenging objects. Compared with the existing forest fire algorithms, the proposed method has higher classification accuracy and speed, with a more comprehensive application range and lower requirements for hardware devices, and these features are worthy of promotion. [ABSTRACT FROM AUTHOR]