<p>Reliable detection of crop diseases is important in early stages for economic, production, and agricultural benefits. This project has outlined computational techniques for soybean crop disease detection. Soft computing methods using Artificial Neural Networks, Decision tree statistical techniques and pattern recognition using k means clustering as well as Linear Discriminent Analysis are used for the accurate; fastest and noise free interpretation in agricultural engineering and elegant performances. Previously, the text-based approach was used, where it observed very tedious and difficult task for solving the purpose of image interpretation. As image collections are growing at a rapid rate, demand for efficient and effective tools for intelligent and optimal classifier from database is required. This paper proposed a novel approach for pattern recognition based on Optimized Feature Vector method which is used for automatic interpretation and classification. Individually K-means clustering NN with Feature Extraction, LDA Based Image interpretation result in elegant performances. The study group included machine learning data set, own developed agriculture data, real farm images, web images. This study confirms that MLP NN artificial neural networks out performs other NNs and can offer a useful approach for developing diagnostic algorithms for soybean diseases.</p>
 
<p>Keywords &ndash; Classification, Soft Computing, Machine Learning, Image Processing, Soybean Disease Interpretation. Feature Extraction Algorithm.</p>