Exploring The Potential of Thermal Imaging for Pre-Symptomatic Diagnosis of Fall Armyworm Infestation In Maize: A Case Study From Ismailia Governorate, Egypt | ||||
Scientific Journal of Agricultural Sciences | ||||
Volume 6, Issue 3, September 2024, Page 103-110 PDF (1.16 MB) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/sjas.2024.312176.1449 | ||||
![]() | ||||
Authors | ||||
M. S. Yones ![]() ![]() | ||||
1National Authority for Remote sensing and Space Sciences (NARSS), 23, Josef proztito St. Elnozha Elgedida - P.O. Box 1564 Alf maskan, Cairo, Egypt | ||||
2Central Laboratory of Residue analyses of Pesticides and Heavy Metals in Food (QCAP), Agricultural Research Center (ARC) | ||||
Abstract | ||||
Early detection of Fall Armyworm (FAW), Spodoptera frugiperda infestation in maize is crucial for minimizing crop losses. This study investigated the potential of thermal imaging as a non-destructive technique for differentiating between healthy and FAW-infected maize plants. Maize samples were collected from Ismailia Governorate, Egypt, during the 2023 growing season. Result succeeded to identify crop monitoring data with the highest priorities for maize in Egypt, Maize cultivation covered approximately 3,096 feddans of the study area, with an average productivity 20 ton / Faddan. So, the total Maize product in the study area is 61,920 ton. Using a Testo IR camera, captured thermal images revealing a significant temperature difference between healthy and diseased maize plants, with infected maize exhibiting an average 3.3°C increase compared to healthy ones. This suggests that FAW infection alters maize structure, potentially impacting temperature. Thermal imaging offers a promising tool for pre-symptomatic diagnosis of FAW infestation in maize, enabling early intervention and improved pest management strategies. These findings highlight the promise of thermal imaging as a non-destructive technique for early detection of FAW infestation. Early and targeted interventions can significantly reduce crop losses and minimize reliance on broad-spectrum pesticides. | ||||
Keywords | ||||
Remote sensing; Spodoptera frugiperda; Monitoring; Predictions; productivity | ||||
Statistics Article View: 198 PDF Download: 141 |
||||