The language flexibility is provided for better understanding of farmers. The dataset of leaf disease has taken from Kaggle site. The mobile application identifies four diseases: Rust, Tar spot, Linden Leaf and Phyllosticta.K. MeghanaRucha GuravKhushboo ParabPreeti Godabole...
D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, N. Batra, PlantDoc: a dataset for visual plant disease detection, inProceedings of the 7th ACM IKDD CoDS and 25th COMAD(2020), pp. 249–253 Google Scholar Dataset.https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf...
The schematic diagram of the proposed methodology has been depicted in Fig.1. The maize plant dataset is accessible on the Kaggle repository [39], notably for images of maize leaf disease. It consists of four subsets of diseases with a total of 4188 images, with 1306 images for common rust...
The experimental data for this study are mainly collected from the Internet, public dataset Rice Leaf Disease Image Samples [12], and relevant rice leaf disease images and labels provided by Kaggle (https://www.kaggle.com). The dataset contains a ...
Plant diseases are one of the biggest challenges faced by the agricultural sector due to the damage and economic losses in crops. Despite the importance, crop disease diagnosis is challenging because of the limited-resources farmers have. Subsequently, t
and analyzing an extensive dataset of cotton leaf images. The primary aim of this dataset is to support automated disease detection systems. We delve into the data collection procedure, distribution of the dataset, preprocessing stages, feature extraction methods, and potential applications. Furthermore...
Fig. 3. Dataset image class examples. This comprehensive dataset stands as a pivotal asset for enhancing disease detection and classification in maize crops. Through meticulous curation, integration of augmented images, and meticulous division for training and testing, it ensures the reliability and eff...
The models were trained using the "Corn or Maize Leaf Disease Dataset" on Kaggle, which included 2930 images of maize leaves. After that, the models were tested using a separate set of 422 images, categorized into four classes: three representing diseases (blight, common rust, and grey leaf...
The chapter focuses on classification of leaf disease in Cassava plants using images acquired real time and from Kaggle dataset. In the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance m...
To address such issues, this paper proposes plants leaf disease detection and preventive measures technique in the agricultural field using image processing and two well-known convolutional neural network (CNN) models as AlexNet and ResNet-50. Firstly, this technique is applied on Kaggle datasets of...