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This study addresses the escalating threat of branched broomrape (Phelipanche ramosa) to California's tomato industry, which provides over 90% of the United States processing tomatoes. The parasite's life cycle, largely underground and therefore invisible until advanced infestation, presents a significant challenge to both detection and management. Conventional chemical control strategies, while widespread, are costly, environmentally detrimental, and often ineffective due to the parasite's subterranean nature and the indiscriminate nature of the treatments. Innovative strategies employing advanced remote sensing technologies were explored, integrating drone-based multispectral imagery with cutting-edge Long Short-Term Memory (LSTM) deep learning networks and utilizing Synthetic Minority Over-sampling Technique (SMOTE) to address the imbalance between healthy and diseased plant samples in the data. The research was conducted on a known broomrape-infested tomato farm in Woodland, Yolo County, California. Data were meticulously gathered across five key growth stages determined by growing degree days (GDD), with multispectral images processed to isolate tomato canopy reflectance. Our findings revealed that the earliest growth stage at which broomrape could be detected with acceptable Accuracy was at 897 GDD, achieving an overall Accuracy of 79.09% and a recall rate for broomrape of 70.36%, without the integration of subsequent growing stages. However, when considering sequential growing stages, the LSTM models applied across four distinct scenarios with and without SMOTE augmentation indicated significant improvements in the identification of broomrape-infested plants. The best-performing scenario, which integrated all growth stages, achieved an overall Accuracy of 88.37% and a Recall rate of 95.37%. These results demonstrate the LSTM network's robust potential for early broomrape detection and highlight the need for further data collection to enhance the model's practical application. Looking ahead, the study's approach promises to evolve into a valuable tool for precision agriculture, potentially revolutionizing the management of crop diseases and supporting sustainable farming practices.
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