Image-based Black Gram Crop Disease Detection

Abstract

The productivity of agriculture is mostly influenced by the Indian economy. Because of the fore mentioned factor, plant diseases are more prevalent in agricultural fields and are easier to identify. Vigilance for the detection of plant diseases has risen due to current agricultural monitoring in numerous and diverse locations. This study presents an image-based method for the Detection of Black gram Crop Disease (DBCD). The Black gram plant is often referred to as “urad” in India and is officially recognized as “Vigna mungo”. This work considers four diseases anthracnose, leaf crinkle, powdery mildew, and yellow mosaic diseases, which have a considerable negative influence on the production of black gram. The black gram crop diseases were classified in this study using the BPLD dataset. For a comparati ve classification analysis, three machine learning algorithms and two deep learning techniques were considered. This classification study for the diagnosis of Black gram crop disease makes use of the artificial neural network and convolutional neural network of deep learning, as well as the decision tree, random forest, and k-nearest neighbor algorithms of machine learning. Here, the accuracy, precision and recall are measured in order to compare various classification models. As per the analysis, CNN outperforms in every aspect when compared to other classifications with 89% accuracy.

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