A DATASET AT A GLANCE...

Dataset for Corn/ Maize Leaf Disease Prediction...

Do you know that maize has been expected to be the most globally-consumed and traded cereal over the next decade, as per the OECD-FAO Agricultural Outlook 2023-2032? However, this expectation can be met only if maize/ corn is increasingly cultivated, without being affected by common plant diseases like, leaf blight or common rust. Though plant diseases can be biologically or chemically- controlled to some extent, their occurrence cannot be completely stopped. So, the careful monitoring of crops for disease identification at an earlier stage is highly-necessitated. However, finding the diseased plants from among the large crop population is not feasible manually. This is where the machine learning- based models render hands.  The reason is: ‘Once an appropriate model is trained with the images of diseased plants or its parts, disease identification becomes easier and accurate for any number of crops’. So, for the researchers willing to put forth machine learning approaches for maize or corn leaf disease identification, there exists a dataset in Kaggle (https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset). This dataset, known as the “Corn or Maize Leaf Disease Dataset”, has been created out of the images from the popular PlantVillage and PlantDoc datasets. The afore-mentioned dataset has about 1162 images of healthy maize or corn leaves, along with 1306, 574, 1146 images of diseased leaves from Common Rust, Gray Leaf Spot and Blight, respectively. Further, there are two citation requests for using the dataset as:

1. Singh D, Jain N, Jain P, Kayal P, Kumawat S and Batra N, “PlantDoc: a dataset for visual plant disease detection”, In Proc: 7th ACM IKDD CoDS and 25th COMAD, Jan 2020, pp. 249-253

2. Geetharamani G and Arun Pandian J, “Identification of plant leaf diseases using a nine-layer deep convolutional neural network”, Computers & Electrical Engineering, 2019, vol. 76, pp. 323-338, doi: 10.1016/j.compeleceng.2019.04.011