Diseases detection in blueberry leaves using computer vision and machine learning techniques

Abstract

This paper explains how image processing techniques and Machine Learning algorithms were used, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Random Forest; and Deep Learning's technique Convolutional Neural Network (CNN) was also used so we can determine which is the best algorithm for the construction of a recognition model that detects whether a blueberry plant is being affected by a disease or pest, or if it is healthy. The images were processed with different filters such as medianBlur and gaussianblur for the elimination of noise, the add Weighted filter was used for the enhancement of details in the images. The images were compiled by the authors of this work, since there was no accessible database of this specific kind of fruit, for which we visited Valle and Pampa farm so we could take pictures of different blueberry leaves, labeled in three different tags: diseased, plagued and healthy. The extraction of characteristics was done with algorithms such as HOG (Histogram of oriented gradients) and LBP (Local binary patterns), both normalized and not normalized. The results of the model showed an 84% accuracy index using Deep Learning, this model was able to classify whether the blueberry plant was beingaffected or not. The result of this work provides a solution to a constant problem in the agricultural sector that affects the production of blueberries, because pests as well as diseases are constant problems in this sector.