Abstract: Plant disease detection is a crucial technology to ensure agricultural productivity and sustainability. However, traditional methods tend to fail as they do not address imprecise and uncertain data in a satisfactory way. We propose the Enhanced Fuzzy Deep Neural Network (EFDNN) which integrate the fuzzy logic with deep neural networks. This study aims to incorporate and allow assessment of the economic impact of the EFDNN on agricultural productivity for plant diseases detection. Data for the research framework were collected from remote sensing and economic sources. Preprocessing of data was done, namely normalization and feature extraction to make sure that the inputs are high quality. Deep Belief Networks (DBNs) were used as a way to pretrain the EFDNN model and supervised learning was then fine-tuned using this. Then, the model was evaluated with accuracy, precision, recall and area under the receiver operating characteristic curve (AUC-ROC), and compared against baseline models: convolutional neural networks (CNNs), traditional DNNs, and fuzzy neural network (FNNs). The plant disease detection performance of the EFDNN model was 95.2% accuracy, 94.8%precision, 95.6% recall, and 0.978 AUC-ROC. The accuracy of the EFDNN model was greater than the accuracy of CNNs by 92.3%, greater than traditional DNNs by 89.7% and FNNs’ accuracy by 90.4%. In economic analysis, however, a reduced pesticide use and an increase in crop yield of USD120 per acre were calculated. 14.3%, leading to higher farmer revenues. The EFDNN model is an effective enhancement to plant disease detection that offers economic and agricultural benefits. This validates the potential of combining fuzzy logic with deep learning to enhance the performance and sustainability of agricultural practices.

Mohammad Abrar, “Enhanced Fuzzy Deep Learning for Plant Disease Detection to Boost the Agricultural Economic Growth” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602133

@article{Abrar2025,
title = {Enhanced Fuzzy Deep Learning for Plant Disease Detection to Boost the Agricultural Economic Growth},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602133},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602133},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Mohammad Abrar}
}