This work provides an extensive review of corn leaves disease prediction. Plant diseases are considered a significant threat to economic loss and production in worldwide agriculture. The monitoring and prediction of conditions play a substantial role in agricultural-based disease prediction. The disease over the plants shows a significant negative impact on crop cultivation. Thus, an automated system is essential for predicting crop diseases, aiming to help the farmers predict disease over the corn leaves. The target of the automated system is to predict the spread of the disease and damages in the plants. The advancements in Artificial Intelligence (AI) pave the way for modern technological improvements for analyzing these conditions in a productive manner where the results show prominence over technological growth. The sub-group of AI is Machine Learning (ML) and Deep Learning (DL) approaches. Both these models work efficiently in disease prediction; however, DL works efficiently with the samples of the vast dataset and gives superior prediction accuracy compared to other approaches. This comprehensive analysis shows a technological path for predicting corn leaves disease more broadly to improve the prediction accuracy and reduce computational complexities. These approaches are well-suited for various real-time studies when resource-constraint devices are used for analysis.
Digital Object Identifier (DOI)
Ashwini, C. and Sellam, V.
"Corn Disease Detection based on Deep Neural Network for Substantiating the Crop Yield,"
Applied Mathematics & Information Sciences: Vol. 16:
3, Article 4.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol16/iss3/4