In agriculture domain, plant disorder identification and its classification are one of the emerging problems to study. If a timely and correct diagnosis is not done, it may lead to adverse effects on agricultural productivity and crop yield. The first sign of disease appears on the leaves. Diseases can be detected from the symptoms appearing on leaves. Aiming at tomato, this paper presents a novel disease recognition convolution neural network architecture based on Self-excitation network and ResNet architecture. The main research gap identified was the use of lab controlled standard images, consideration of only biotic disorders and low accuracy on unseen test dataset. The main contribution of this work is to increase generalization. Therefore, to reduce generalization error, augmentation is applied and images are captured in a manner where leaf is surrounded by occlusion areas. To capture minute lesion and spot details, multiscale feature extraction with dilated kernel is applied. Our collected real-world dataset consists of 11 types of biotic and abiotic disorders. Various experiments are carried out to verify proposed method’s effectiveness. The proposed method has a recognition accuracy of 81.19% on a real-world validation dataset using 75-10-15(train-validation-test) division ratio on augmented data and average recognition accuracy of 91.76% for the 10-fold cross-validation technique. The comparative analysis with all state-of-the-art techniques exhibited amelioration in the computation time and classification accuracy. The results are used to classify tomato biotic and abiotic diseases in the real-world complex environment and novelty lies in the fact that both biotic and abiotic elements are taken into account.