Title

Sustainable Environment Preservation: Early Detection of Red Palm Weevil Infestations Using Deep Learning Classification of Acoustic Signals

Participant's Name:

Dr. Wadii Boulila

Department:

Computer Sciences

Why This Project Deserves the Award?

The proposed approach presents several noteworthy contributions:
• First, the combination of the three selected sound features (CQCC, MFCC, and BFCC) resulted in improved classification accuracy for RPW detection.
• The method allows for the early detection of RPW infestations, enabling prompt intervention by farmers and professionals.
• The experimental results demonstrated superior performance compared to existing techniques for public datasets.

How the proposed study is linked to the climate change challenge?
Climate change poses significant threats to global food security, ...agriculture, and natural ecosystems. One of the critical impacts of climate change on agriculture is the increased prevalence of pests and diseases, which can devastate crops, reduce yields, and lead to food shortages. The Red Palm Weevil (RPW) is a destructive pest that poses a significant threat to date palm trees, an essential agricultural crop in arid regions. RPW infestations can cause severe damage to palm trees, leading to economic losses for farmers and disruption of the ecosystem.
The proposed solution leverages deep learning and acoustic signal classification to provide early detection of RPW infestations in date palm trees. By detecting infestations at an early stage, farmers can take prompt and targeted intervention measures to control the pest's spread and minimize crop losses. This early detection capability is crucial in the context of climate change, where pests and diseases are expected to become more prevalent due to changing weather patterns and ecological shifts.

Why the proposed paper deserves the award?
1. The proposed approach aligns perfectly with the objectives of the CLIMATE CHANGE RESEARCH AWARDS. Climate change poses significant challenges to agricultural systems worldwide, and the effective detection and control of pests are critical for ensuring food security and sustainable farming practices. This research contributes to the preservation of the environment by reducing the use of chemical pesticides and minimizing the environmental impact caused by RPW infestations.
2. The proposed study is under minor revision in the journal Computers and Electronics in Agriculture, with an impact factor of 8.3. This journal is a reputable and respected journal in agriculture and its intersection with computer science and electronics. The journal focuses on the application of technology and digital solutions in agriculture, including precision farming, remote sensing, data analytics, robotics, artificial intelligence, and various other advanced technologies.
3. The proposed research has been submitted to the Saudi Authority for Intellectual Property (SAIP) to be patented and it is in the final approval stage.
4. The proposed system provides a tangible solution to climate change through its focus on sustainable agriculture and environmentally friendly pest management.

practices:
• Sustainable Pest Management:
The early detection of RPW infestations allows for targeted and sustainable pest management practices. Instead of relying on the widespread and indiscriminate use of chemical pesticides, which can have adverse environmental impacts, farmers can focus on targeted interventions. This approach minimizes the use of harmful chemicals, preserves natural ecosystems, and promotes more sustainable agricultural practices.
• Reduced Environmental Impact:
By employing acoustic signal classification and deep learning techniques, the proposed solution minimizes the need for physical inspections or extensive monitoring efforts, which can be resource-intensive and environmentally burdensome. The use of non-invasive sound data for detection ensures a low impact on the environment while effectively combating the RPW infestation.
• Enhanced Food Security:
Date palm trees play a significant role in the food security of arid regions. By preserving date palm trees from RPW infestations, the proposed solution contributes to the stability and reliability of food production in these areas. A secure food supply is crucial in the face of climate change-induced challenges, such as extreme weather events and changing agricultural conditions.
• Economic Benefits:
Early detection and intervention against RPW infestations lead to reduced crop losses and increased agricultural productivity. This, in turn, boosts the income of farmers and strengthens the agricultural sector's resilience to climate-related challenges.
• Promoting Technological Solutions:
The proposed approach demonstrates the potential of advanced technologies, such as deep learning and acoustic signal classification, in addressing climate change challenges. It sets an example for integrating innovative solutions into agricultural practices to enhance environmental sustainability.

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