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Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

This paper presents for the first time the design of a microwave sensing setup for the potential monitoring and identification of red palm weevil (RPW) gender type. The microwave sensor consists of a planar two-port transmission line (TL) with a single complementary split-ring resonant (CSRR) inclusion etched from the bottom metallic layer. The CSRR sensor is placed on top of a customized non-conductive container. The microwave sensing setup was designed, numerically demonstrated, fabricated and tested experimentally. Simulated results correlate quite well with the experimental data. Moreover, the sensitivity of the CSRR sensor when in close proximity to different RPW genders was evaluated both numerically and experimentally. Based on the measured results from 15 RPW samples with different body sizes, different RPW gender types showed unique microwave signatures. A notable shift in the sensor’s resonance frequency was achieved, where on average a resonant frequency shift of 10% for adult RPWs was achieved, while a 2.4% frequency change was obtained for larvae (young) RPWs. Hence, the proposed microwave sensing setup can be adopted in field trials to examine and differentiate between various RPW genders at various developmental stages.

Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100–800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between “infested” and “healthy” signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system’s ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.

Background The Red Palm Weevil (RPW) Rhynchophorus ferrugineus (Oliver) is one of the most damaging invasive insect species in the world. This weevil is highly specialized to thrive in adverse desert climates, and it causes major economic losses due to its effects on palm trees around the world. RPWs locate palm trees by means of plant volatile cues and use an aggregation pheromone to coordinate a mass-attack. Here we report on the high throughput sequencing of the RPW antennal transcriptome and present a description of the highly expressed chemosensory gene families. Results Deep sequencing and assembly of the RPW antennal transcriptome yielded 35,667 transcripts with an average length of 857 bp and identified a large number of highly expressed transcripts of odorant binding proteins (OBPs), chemosensory proteins (CSPs), odorant receptors/co-receptors (ORs/Orcos), sensory neuron membrane proteins (SNMPs), gustatory receptors (GRs) and ionotropic receptors (IRs). In total, 38 OBPs, 12 CSPs, 76 ORs, 1 Orco, 6 SNMPs, 15 GRs and 10 IRs were annotated in the R. ferrugineus antennal transcriptome. A comparative transcriptome analysis with the bark beetle showed that 25 % of the blast hits were unique to R. ferrugineus , indicating a higher, more complete transcript coverage for R. ferrugineus . We categorized the RPW ORs into seven subfamilies of coleopteran ORs and predicted two new subfamilies of ORs. The OR protein sequences were compared with those of the flour beetle, the cerambycid beetle and the bark beetle, and we identified coleopteran-specific, highly conserved ORs as well as unique ORs that are putatively involved in RPW aggregation pheromone detection. We identified 26 Minus-C OBPs and 8 Plus-C OBPs and grouped R. ferrugineus OBPs into different OBP-subfamilies according to phylogeny, which indicated significant species-specific expansion and divergence in R. ferrugineus . We also identified a diverse family of CSP proteins, as well as a coleopteran-specific CSP lineage that diverged from Diptera and Lepidoptera. We identified several extremely diverged IR orthologues as well as highly conserved insect IR co-receptor orthologous transcripts in R. ferrugineus . Notably, GR orthologous transcripts for CO 2 -sensing and sweet tastants were identified in R. ferrugineus , and we found a great diversity of GRs within the coleopteran family. With respect to SNMP-1 and SNMP-2 orthologous transcripts, one SNMP-1 orthologue was found to be strikingly highly expressed in the R. ferrugineus antennal transcriptome. Conclusion Our study presents the first comprehensive catalogue of olfactory gene families involved in pheromone and general odorant detection in R. ferrugineus , which are potential novel targets for pest control strategies.

Infesting a variety of palm trees throughout Egypt, the Red Palm Weevil (RPW), Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae), is regarded as a damaging pest. The objective of the current study was to determine the protein pattern while evaluating six essential oil emulsions as botanical extracts against this pest in a laboratory setting. The method of dipping food was used at concentrations of 2, 3, 4, 5 and 6%. There were notable variations between the various oils and exposure times. The studied oils showed varying fatality rates in response to increasing concentrations. Across all concentrations, the lowest mortality was obtained with citronella oil. After 96 hours, 6% of the clove and orange oils caused 100% death. Totally 90% of insect died when exposed to 4% concentration of chili oil. Even though the tested oils had different fatality rates, longer exposure times resulted in larger percentages. The molecular weights of the bands varied and ranged from 7 to 275 KD in the treated hemolymph and from 6 to 273 KD in the untreated hemolymph, indicating significant variations between the two types of hemolymph. It is stated that essential oils are a good choice for RPW control approaches, as long as the suitable type of oil is used, taking into account exposure time and oil concentration.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases.

Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most critical pests of palms is the red palm weevil, which is an insect that causes much damage to palm trees and can devastate vast areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a pressing need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed an IoT-based smart palm monitoring prototype as a proof-of-concept that (1) allows monitoring palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil infestation. Users can use web/mobile applications to interact with their palm farms and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer. Moreover, we have collected data using accelerometer sensors, and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.

The Red Palm Weevil (RPW) is one of the most destructive pests affecting palm trees worldwide, leading to severe agricultural and economic losses. Early detection was essential for effective intervention; however, conventional methods such as pheromone traps and manual inspections frequently failed to identify early stage infestations.To address this challenge, this study developed an automated RPW detection framework using thermal image processing and deep learning.A Convolutional Neural Network(CNN) model was trained to analyze thermal images and detect early signs of infestation.The proposed model achieved a detection accuracy of 98.5%,outperforming traditional machine learning techniques in both precision and response speed. For real-time deployment, the trained CNN was integrated into a Raspberry Pi 4B, enabling a low cost, scalable, and non-invasive monitoring solution suitable for field applications.While the system demonstrated strong performance under controlled conditions, the work also identified key limitations, including the limited penetration depth of thermal imaging and the need for large-scale field validation to ensure robustness under diverse real-world environments.

Red Palm Weevil(RPW) infestation is a major challenge in palm production, often remaining undetected until severe internal damage has occurred. This proposed work presents a novel non-invasive auditory detection system that combines advanced signal processing with deep learning for the early identification of RPW. Acoustic signals from palm trees were pre-processed to suppress noise, and Linear Predictive Coding (LPC) was used to extract spectral features specific to RPW activity. To refine feature matching, cosine similarity was applied, followed by classification using a Bidirectional Long Short-Term Memory (Bi-LSTM) network. The proposed approach achieved an accuracy of 98.02%, outperforming traditional detection techniques. The novelty of this work lies in integrating LPC based features, cosine similarity, and Bi-LSTM for temporal pattern recognition, enabling highly reliable early detection. A Bi-LSTM RPW classifier combining LPC and cosine similarity, improving accuracy over conventional shallow models was developed. The robust RPW detection system provides accurate outputs despite noise, interference and signal distortion. A limitation of this work is that the evaluation was performed on a controlled dataset, and large scale field validation is required for wider applicability.