A Dataset for Early Detection of Corn Leaf Pests in Precision Agriculture
Description
Corn plantations were visited to collect images of both infected and healthy leaves. An agricultural expert labeled the samples as follows: "S" for images containing only healthy leaves, "M1" for leaves infected by Spodoptera frugiperda, "M2" for leaves infected by Helminthosporium leaf blight, and "M3" for leaves infected by Zonocerus variegatus. The original dimensions of the images varied: 4248 × 5664, 1920 × 2560, 2448 × 3264, 3120 × 4160, and 2448 × 3264. All the images were resized to 400 × 400 pixels using Python programming. The resizing process maintained the quality of the images. In total, 1308 images were collected from various fields, resulting in a dataset containing healthy leaves, and leaves infected by Spodoptera frugiperda, Helminthosporium, and Zonocerus variegatus. Each image was labeled according to the following format: field_code – image_condition – growth_stage – date_taken – sequence_number. This coding was implemented to facilitate the tracking of the state of each corn leave within the dataset. Note that we published a paper titled "Automatic Segmentation Based on the CNDVI (Combined Normalized Difference Vegetation Index)" (see [1]) using an initial, small version of this dataset. The work in [1] focuses on background segmentation of corn plants in the field. The algorithm proposed in [1] was further applied to the dataset to segment the background of corn by removing all background elements, leaving only the corn leaf in the image. Another version of background segmentation is also proposed in this paper, using manual segmentation. Additionally, we introduce a dataset that highlights all infected areas on the corn leaf. Overall, we propose 8 datasets described as follows: Dataset 1: Natural images Dataset 2: Images with manually segmented backgrounds Dataset 3: Images with automatically segmented backgrounds by CNDVI Dataset 4: Augmented version of Dataset 1 by a factor of 9 Dataset 5: Augmented version of Dataset 2 by a factor of 9 Dataset 6: Augmented version of Dataset 3 by a factor of 9 Dataset 7: Images of infected leaves with manually segmented backgrounds and infected areas highlighted Dataset 8: Augmented version of Dataset 7 by a factor of 9