R. Prabha J. S. Kennedy G. Vanitha N. Sathiah M. Banu Priya


Several pests and diseases wreak havoc on maize crops worldwide. Novel and rapid methods for detecting pests and diseases in real-time will make monitoring them and designing effective management measures easier. In the recent past, maize has been imperilled by fall armyworms (Spodoptera frugiperda), which have caused substantial yield losses in maize. This study aimed to create an Android mobile application via  DCNN (Deep Convolutional Neural Network)-based AI pest detection system for maize producers. Everyone benefits from the deployment of these CNN models on mobile phones, especially farmers and agricultural extension professionals because it makes them more accessible. Automatic diagnosis of plant pest infestations from captured images through computer vision and artificial intelligence research is feasible for technological advancements. Therefore, early detection of maize fall armyworm (FAW) infestation and providing relevant recommendations in maize could result in intensified maize crop yields. . An Android mobile application was created to identify fall armyworm infection in maize and included the recommendations given by Tamil Nadu Agricultural University proposed Integrated Pest Management (TNAU IPM ) capsules in the mobile app on as to how to deal with such a problem. Digital and novel technology was chosen to address these issues in maize. Deep convolutional neural networks (DCNNs) and transfer learning have recently moved into the realm of just-in-time crop pest infestation detection, following their successful use in a variety of fields. The algorithm accurately detects FAW (S. frugiperda) infected areas on maize with 98.47% training accuracy and 93.47% validation accuracy.


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Artificial intelligence, Deep convolutional neural networks, Fall armyworm, Mobile app.

Amara,J., Bouaziz, B & Algergawy, A. (2017). A Deep Learning-based Approach for Banana Leaf Diseases Classification,". Database systems for business, technology and web,79–88.
Barbedo. A. (2014). An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing. Plant Disease. 98 (12), 1709–1716. https://doi.org/10.1094/PDIS-03- 14-0290-RE.
Brahimi, M., Boukhalfa,K & Moussaoui, A. (2017). Deep Learning for Tomato Diseases: Classification and Symptoms Visualization, Applied Artificial Intelligence, 31(4)., 299–315.https://doi.org/10.1080/08839514.2017.13 15516.
Dyrmann, M., Karstoft, H & Midtiby, H.S. (2016). Plant species classification using deep convolutional neural network, Biosyst. Eng, 72–80.
Eli-Chukwu. C. (2019). Applications of Artificial Intelligence in Agriculture: A review, Engineering, Technology & Applied Science Research. 9(4), 4377–4383, https://doi.org/10.48084/etasr.2756.
Fuentes, A., Yoon, S., Kim, S.C & Park, D.S. (2017). A Robust Deep Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. Sensors, 17 (9). https://doi.org/10.3390/s17092022.
Hidayat, U., Darusalam & Irmawati, I. (2019). Detection of disease on corn plants using convolutional neural. Journal of Computer Science and Information, 12(1), 51-56.
Kulkarni, O., (2018). Crop disease detection using deep learning. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-4, IEEE.
Mkonyi, L., Rubanga, D., Richard, M., Zekeya, N., Sawahiko, S., Maiseli, B. & Machuve, D., (2020). "Early identification of Tuta absoluta in tomato plants using deep learning," Scientific African. https://doi.org/10.1016/j.sciaf.2020.e00590.
Mohanty, P., Hughes, P & Salathe, M. (2016). "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, 7. https://doi.org/10.3389/fpls.2016.01419.
Patricio, I & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review, Computers and Electronics in Agriculture, 53, 69–81. https://doi.org/10.1016/j.compag.20 18.0 8.001.
Ramcharan, K., Baranowski, P., McCloskey, B., Ahmed, J., Legg & Hughes, P. (2017). Deep Learning for Image-Based Cassava Disease Detection, Frontiers in Plant Science, 8. https://doi.org/10.3389/fpls.2017.01852.
Sibiya, M & Sumbwanyambe, M. (2019). A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks, Agri Engineering, 1(1), 119-131.
Sladojevic,S., Arsenovic, M., Anderla, A., Culibrk, D. & Stefanovic, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification, Computational Intelligence and Neuroscience. https://doi.org/10.1155/2016/3289801
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Prabha, R., Kennedy, J. S., Vanitha, G., Sathiah, N., & Priya, M. B. (2022). Android application development for identifying maize infested with fall armyworms with Tamil Nadu Agricultural University Integrated proposed pest management (TNAU IPM) capsules. Journal of Applied and Natural Science, 14(SI), 138–144. https://doi.org/10.31018/jans.v14iSI.3599
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