Improving Tomato Disease Classification Using BR-TomatoCNN: An Efficient Model Utilizing Bottleneck Residuals U. Shruthi, V. Nagaveni, Sunil G. L. Journal of Advances in Information Technology, 2024 —Tomatoes represent a globally significant and commercially valuable crop, yet they are susceptible to a multitude of diseases that can significantly reduce their production and quality. To address this critical issue, we have introduced the BR-TomatoCNN, a novel lightweight Convolutional Neural Network (CNN) model that uses Bottleneck Residuals (BR) to increase the classification accuracy of tomato diseases. This research includes a comprehensive examination of how various optimizers influence the proposed model’s performance using evaluation metrics such as accuracy, loss, precision, recall, and F1 − Score. A dataset consisting of nine distinct tomato disease classes collected from the Plant Village repository and the Powdery Mildew disease class was prepared with the help of farmers and experts. That was used to train the proposed model achieved remarkable results of 99.82% accuracy and an F1 − Score of 1.00. These findings not only underscore the BR-TomatoCNN’s capability to accurately identify tomato diseases but also position it as a superior alternative to existing methodologies and pre-trained models. Our study underscores the significance of exploring a new approach, such as utilizing bottleneck residuals to improve the accuracy of the classification model. BR-TomatoCNN promises to play a pivotal role in disease management in the agricultural sector by facilitating early disease detection. This advancement in technology has the potential to enhance tomato crop yields and overall produce quality.
A Review on Prediction of Crop Yield using Machine Learning Techniques Sunil G L, Nagaveni V, Shruthi U 2022 IEEE Region 10 Symposium Tensymp 2022, 2022 Population is increasing day by day in India and demand for food is also increasing, due to this reason, agriculture is very essential. Advanced technologies like data mining techniques, machine learning techniques, remote sensing and image processing, etc., can be used for prediction of crop yield. Machine Learning techniques play a significant role in crop yield prediction using different parameters like temperature, rain fall, soil parameters, area of sowing, etc,. This paper carried out a comparative study on various machine learning techniques like supervised and unsupervised learning methods to predict crop yield. It has been observed that classification techniques give high accuracy in prediction of multiple crop yields with different parameters than regression and unsupervised learning methods
Apple Varieties Classification using Light Weight CNN Model U Shruthi, Kalidindi Sai Narmadha, E Meghana, D N Meghana, K P Lakana, et al. 4th International Conference on Circuits Control Communication and Computing I4c 2022, 2022 Fruit classification is a vital task in many industrial contexts. The automated fruit classification system is becoming more and more important in the food-processing sector. In this paper, we offer a method for sorting apples using deep learning technology. Deep learning models can achieve cutting-edge accuracy, sometimes even outperforming human ability. Using convolutional neural network (CNN) architecture, the proposed model will be able to identify 14 different varieties of apples. For feature extraction, five layers of two residual blocks are used. Artificial neural network (ANN) based neurons used for the classification of apple varieties in fully connected layers. The CNN layers used in residuals are a combination of convolutional layer, non-linearity layer, pooling layer, and fully connected layer. When compared to ResNet50, VGG16, MobileNet, and EfficientNetB0 state-of-the-art architectures, the proposed approach is able to obtain improved training accuracy of 99.95%, validation accuracy of 99.02%, and testing accuracy of 99.59% with adam optimizer.
A Review on Machine Learning Classification Techniques for Plant Disease Detection U. Shruthi, V. Nagaveni, B.K. Raghavendra 2019 5th International Conference on Advanced Computing and Communication Systems Icaccs 2019, 2019 In India, Agriculture plays an essential role because of the rapid growth of population and increased in demand for food. Therefore, it needs to increase in crop yield. One major effect on low crop yield is disease caused by bacteria, virus and fungus. It can be prevented by using plant diseases detection techniques. Machine learning methods can be used for diseases identification because it mainly apply on data themselves and gives priority to outcomes of certain task. This paper presents the stages of general plant diseases detection system and comparative study on machine learning classification techniques for plant disease detection. In this survey it observed that Convolutional Neural Network gives high accuracy and detects more number of diseases of multiple crops.
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