Abstract
Crop diseases are a significant danger to food security, yet their quick identification remains difficult and sometimes, unachievable in various regions because of the absence of necessary infrastructure. Distinguishing a malady effectively when it initially shows up is a critical advance for constructive disease management. Existing methods to identify diseases include local plant clinics, agricultural organizations and institutions and online platforms, which prove to be quite tedious and time-consuming. New techniques have been emerging in the domain of leaf-based image detection which have produced outstanding outcomes through machine learning algorithms. In any image processing machine learning model, the feature extraction methods used can adversely affect the overall performance of the model. So, this project aims at comparing different feature extraction techniques using the SVM classifier.
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Tiwari, V., Agrahari, A., Srivastava, S. (2021). Performance Analysis of Hand-Crafted Features and CNN Toward Real-Time Crop Disease Identification. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_48
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