A Feature Extraction and Machine Learning Based Approach for Automated Identification of Crop Diseases

A Feature Extraction and Machine Learning Based Approach for Automated Identification of Crop Diseases

Kamalsingh Solanki1 Prof. Devkinandan Nagar2

Abstract: With machine learning being used in agricultural applications, a new domain of science has emerged which is termed as precision agriculture. It is the amalgamation of data science, analytics, AI and ML technologies for enhancing conventional agricultural practices. This paper addresses the challenge of identifying blight (late and early) based on a machine learning approach. In this approach, the image is first pre-processed to convert from RGB to Grayscale and subsequently denoised. Next the statistical features of the image are computed to train a machine learning models based on a probabilistic approach employing the Bayes Theorem of conditional probability. A penalty factor is included for the training purpose termed as regularization which optimise the weight updated mechanism.  The final classification accuracy is computed based on the TP, TN, FP and FN rates which yield a classification accuracy of 97.69%.

Keywords— Potato Leaf Disease (blight), Image Denoising, Feature Extraction, Deep Neural Networks, Classification Accuracy.