Computer-aided fusion-based neural network in application to categorize tomato plants

Pest’s infection affects the crop production and annual income. From the past decade, many traditional methods anticipated the optimum accuracy while categorizing the infected tomato-plants. Every technique has their pros and specifically the cons. As an upgradation, this paper introduces appropriate unsupervised detection & categorization of the diseased/healthy tomato plant using neural-net techniques. Image dataset is congregation of both online and naturally accessible samples for healthy & diseased tomato crops. The current algorithm executes three steps to attain utmost performance: (i) Data pre-processing using Non-Subsampled Contourlet to acquire energy-detail components, (ii) Modified K-means processing to extract colored clusters, that are in-turn utilized for tomato-leaf detection, and (iii) finally Modified Convolution-Neural Network features are fused to SVM for automated categorization. The work was tested for Kaggle PlantVillage and Mendeley datatset constituting 20,283 images, forming one healthy and 10 disease classes. The model undergoes the subjective performance metric evaluation and achieved the model accuracy as 99.15% and average precision of 95.6%. Technique produces highly intense, automatic and accurate classifier results over state-of-the-art approaches.