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The plants diseases affect both the production and quality of food in the agriculture sector. Computer vision techniques can contribute significantly by detecting the plant’s diseases at very early stages with more accuracy.

In this work, we proposed an automated crop disease recognition system using partial least squares (PLS) regression for feature selection from an extracted deep feature set. The presented framework incorporates three primary phases: First, the deep features are extracted using a pre-trained Visual Geometry Group (VGG19) convolutional neural networks (CNN) model; Second, a PLS-based parallel fusion method combines the features extracted from the fully connected layers 6 and 7; Third, the best features are selected using a PLS projection method. The most discriminant features are finally plugged into the ensemble baggage tree classifier for final recognition.

Three …