Highly Précised Crop Monitoring and Animal Attack Prevention System
The nonspecific nature of the leaf signs over various pest attacks and nutrient deficiency gives raises prominent techniques to diagnosis it. Animal attacks are also posed vital threats due to damages causes and all other subsequent impact on crop productivity. Careful examination of leaf pattern is the only way to effective diagnosis of crop defection and sensor equipped microcontroller system is the only way for avoiding animal attacks. Spatial leaf metric analyzes of crops in remote sensing imagery has also improved dramatically in recent years. The proposed method differs from others in: 1) the simplicity of the developed approach; 2) classification of complete leaf images with various feature attributes ; and 3) use of sensor components to control animal attacks and detect it using automated control system. Recently, a nonparametric approach to texture analysis has been developed, in which the distributions of simple texture measures based on local ternary patterns (LTP) are used for texture description. This paper shows that a properly selected subset of texture patterns along with color converted transform features and shape parameters which can achieve better classification rates in comparison with the existing methods. Computer simulation involved the following tests: comparing the impact of multi modal feature set on the plant nutrient deficiency with the influence of texture pattern and carry out automated notification system which comprise of GSM modem and validate the hardware results with the USB serial interface with auto classification system.