Identification of rice crop diseases using gray level co-occurrence matrix (GLCM) and Neuro-GA classifier

The timely detection and identification of crop diseases is a crucial aspect of the agricultural sector. It contributes significantly to the by and large productivity of the plant. One of the most crucial factors that we need to consider while determining a plant’s susceptibility to a particular disease is the visual characteristics of the affected plant. The increasing popularity of automation and availability of efficient techniques for disease identification has led to the development of novel methods and engraved impactful technologies in field of automated disease detection. The traditional methods have not been able to provide the researchers with the most accurate results. The proposed model in this work can identify the rice crop disease without relying on subjective data and have many advantages over traditional approaches as evident from the results derived. It has the potential to improve the efficiency of the process and aid in early detection. Machine learning method presents real-time automated decision support systems and can help improve crop or plant growth productivity and quality. This work aims to introduce a new and enhanced method as Neuro-GA, which is a combination of both the artificial neural network (ANN) and the genetic algorithm (GA). It has been claimed that it is more powerful and accurate than the traditional methods. The pioneer and nascent stages of this analysis includes preprocessing of the data was carried out. The features were then extracted using Gray-level co-occurrence matrix (GLCM) and subsequently the finally extracted features were cascaded to the Neuro-GA classifier. The digital image processing (DIP) techniques used in this study for rendering visual images along with Neuro-GA classifier resulted in skyrocket accuracy level of 90% and above. The technique validated in this study has allowed the automated monitoring of various aspects of crop production and farming and an omnipotent promising efficiency hence this approach can be magnanimously effective in monitoring agricultural production and thereby plummeting waste allied with crop damage.