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Multi-Channel Convolutional Neural Network for Prediction of Leaf Disease and Soil Properties

: Prediction of leaf disease and soil property helps farmers improve crop production quality through maintaining the soil property and taking proper actions for leaf disease. Various techniques have been developed to predict leaf disease and soil properties. Support Vector Machine (SVM) is one of the machine learning techniques that was used to predict leaf disease and soil properties. To predict the leaf disease and soil property, SVM processed the extracted features from leaf images and soil images. Deep learning can be used for prediction, which has the advantage of machine learning is that one does not need to be concerned about domain knowledge as no feature engineering is required in this, unlike SVM-based prediction. In addition to this, SVM-based prediction is not very effective for handling multiple inputs. A Convolutional Neural Network (CNN) is a deep learner which was applied for the prediction of leaf disease and soil property. Even though this method has better performance, the information from leaf and soil images is mixed together, which may affect the prediction accuracy. So, in this paper, a Multi-channel CNN (MCNN) method is introduced in which individual channels are used for leaf and soil images. In MCNN, the feature learning using MCNN for leaf images is kept distinct from the soil image to avoid data fusion between the leaf and soil images. The features related to leaf and soil images are paired and transferred over the corresponding channels for the prediction of leaf disease and soil property. After the prediction of leaf disease and soil property, the correlation between leaf disease and soil property is identified using the Pearson correlation coefficient and it is sent to the farmers using mobile phones to improve their crop production. Finally, these methods are validated by using different leaf infections and soil images for 3 types of crops. The experimental results show that the MCNN method achieves an average accuracy of 87.77% for leaf disease prediction and 90.38% for soil property prediction compared to the classical methods.