Crop disease monitoring and recognizing system by soft computing and image processing models
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Crop disease monitoring and recognizing system by soft computing and image processing models Shanwen Zhang 1 & Wenzhun Huang 1 & Haoxiang Wang 2 Received: 19 January 2020 / Revised: 5 August 2020 / Accepted: 11 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In order to make the crop disease intelligent diagnosis system more cheap, convenient and efficient for common farmers, an effective and general monitoring system of crop diseases is constructed by Internet of Things (IoT). In the system, crop disease images are collected by IoT and passed to the Web server by wireless network. First, a crop disease image dataset is constructed. Second, a K-mean clustering algorithm is utilized to segment disease leaf images, and the sum and difference histogram (SADH) feature vector is extracted from each segmented defect image based on the intensity values of the neighboring pixels. Finally, a reasoning process is built on the reasoning decision tree for monitoring system. The results validates that the proposed system is benefit for monitoring and controlling crop diseases in practice. Keywords Crop disease recognition . Crop disease monitoring system . Internet of things (IoT) . Sum and difference histogram (SADH)
1 Introduction Currently, more than 100 crop diseases found in China often result in a dilemma as they cause significant losses in yield and quality of crop products. In recent years, abnormal ecological environment has brought large difficult to prevent and control crop diseases. There are different traditional mechanisms to identify and classify crop leaf diseases by chemical analysis or visual observation. But, they take more time, require professional staff, and are sensitive to human error and variation. Automatic crop disease detection and recognition can be implemented using a lot of technologies such as artificial intelligence, IoT, machine learning regression techniques, image processing, transfer learning, hyper-spectral imagery,
* Haoxiang Wang [email protected]
1
School of Information Engineering, Xijing University, Xi’an 710123, China
2
GoPerception Labs, Ithaca, NY 14853, USA
Multimedia Tools and Applications
leaf image extraction and segmentation [3, 11, 15]. As the development of computer and IoT technologies, it is possible to monitor diseases in large area of farmland. IoT can guide the plant protection experts to monitor crop diseases at anytime and anywhere [19]. The smart agriculture expert system based on IoT has gradually become a research topic. Wang et al. [17] provided a survey of crop disease detection using image processing and wireless sensor network/wireless multimedia sensor network techniques in precision agriculture field. This survey will promote an emulation based architecture that is able to combine these different emerging technologies practically. In order to meet the needs of modern agriculture with IoT and related technologies, Zahid et al. [22] overviewed the automatic disease detection and classification methods, wh
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