PestDetection

PEST-DETECTION-IN-AGRICULTURAL-FIELD

TABLE OF CONTENT

OVERVIEW

https://github.com/SunilPrasad31/PestDetection/assets/145242357/89859b1f-a102-4b7b-a430-887463ca9499

# INTRODUCTION

Agriculture, a bedrock of human civilization, represents the intricate fusion of art and science dedicated to cultivating the soil for the production of essential sustenance, animal feed, fibers, and an array of vital products. The profound significance of agriculture in sustaining global populations underscores the critical imperative of prioritizing and optimizing crop health and productivity. Amidst the myriad challenges faced by farmers, the looming threat posed by pests—ranging from insects and diseases to persistent weeds—constitutes a formidable adversary capable of compromising crop yields and imperiling food security.

In response to this challenge, agricultural pest detection emerges as an indispensable pillar of modern farming practices. Harnessing the prowess of state-of-the-art technologies, including advanced sensors, high-resolution imaging, and sophisticated data analysis, this facet of agricultural science endeavors to swiftly and precisely identify potential threats to crops. The overarching objective is to empower farmers with actionable insights, enabling them to execute targeted and timely interventions to safeguard their crops from the detrimental impact of pests.

This proactive and technology-driven approach not only augments agricultural yields but also serves as a vanguard for sustainable farming practices. By mitigating the reliance on broad-spectrum pesticides and embracing precision interventions, agricultural pest detection becomes an instrumental force in cultivating not just crops, but also long-term ecological health and resilience. In essence, it epitomizes a holistic strategy that harmonizes technology, scientific understanding, and environmental stewardship to fortify the foundations of global agriculture.

OBJECTIVE

The primary goal of agricultural pest detection is to proactively identify and monitor potential threats to crops, specifically focusing on the early identification of detrimental insects. This process harnesses an array of cutting-edge technologies, including advanced sensors, high-resolution imaging, and sophisticated data analysis methods. By leveraging these technological tools, the agricultural industry aims to establish a comprehensive and real-time monitoring system for pest activities within crop fields.

This strategic approach serves as a crucial component of modern precision agriculture, enabling farmers to promptly detect and assess the presence of harmful insects that could jeopardize crop health. Timely and accurate identification of pest infestations empowers farmers with actionable insights, allowing them to implement targeted interventions and deploy precise pest management strategies. Such proactive measures not only mitigate potential crop losses but also contribute to sustainable farming practices by minimizing the reliance on broad-spectrum pesticides.

Ultimately, the integration of technology in agricultural pest detection fosters a more resilient and productive farming ecosystem. This technological synergy enhances overall crop yields, promotes resource efficiency, and aligns with the broader objectives of sustainable and environmentally conscious agricultural practices.

WHY ONEAPI

20240130_234550

oneAPI’s multifaceted optimization strategy for deep learning is meticulously designed to transcend performance across an expansive spectrum of diverse hardware environments, deploying key components with unparalleled precision and strategic acumen. At its epicenter lies the sophisticated oneDNN library, intricately fine-tuning primitives for fundamental operations within deep neural networks. This optimization prowess seamlessly extends across the intricate architectures of Intel CPUs, GPUs, and FPGAs, ensuring not just efficiency but the pinnacle of computational power.

Empowering developers on this transformative journey is Data Parallel C++ (DPC++), a groundbreaking tool that allows an eloquent expression of parallelism in code. This yields solutions that gracefully adapt to the intricacies of various hardware architectures, forming a symphony of computational efficiency.

The dynamic Intel GPU offload support ingeniously shifts compute-intensive tasks to GPUs, unlocking their formidable parallel processing capabilities and propelling accelerated deep learning workloads into the realms of unparalleled efficiency. This dynamic interplay of hardware prowess unleashes a new era of computational potential.

Moreover, this strategic optimization places a distinct emphasis on advanced vectorization and parallelization techniques. Deftly harnessing modern hardware features, it doesn’t just amplify throughput but reshapes the landscape of execution times, substantially reducing them.

This comprehensive approach isn’t just a strategy; it’s a transformative journey. Enshrining specialized libraries, high-level abstractions, and the utmost efficiency in hardware utilization, it stands as an advanced framework that empowers developers to not only attain but surpass unparalleled performance and portability in their intricate deep learning applications. In the symphony of computational excellence, oneAPI orchestrates a harmonious convergence of technology and innovation.

FLOW CHART

Screenshot_20240131_012218_Microsoft 365 (Office)

* Start: Capture images of pests in the agricultural field.

* Process: Analyze the captured images.

* Match Found?
     * No: Pest not detected.
     * Yes: Proceed to the next step.
* Detected Pest with Accuracy: Identify and report the detected pest with accuracy.

* Pest Not Detected: If no pest is detected, take necessary actions.

* Stop: End the process.

HOW TO RUN THIS PROJECT ?

Steps to run this project:

STEP 1: Download the models and the dataset from the drive link provided below.

STEP 2: Clone the GitHub repository.

STEP 3: Run the app.ipynb in the terminal of the project folder.

DATASET

https://github.com/SunilPrasad31/PestDetection/assets/145242357/e976d126-0095-44f6-b926-b70753aaaa56

FUTURE SCOPE

The future scope in agricultural pest detection involves advancements in technologies like AI, machine learning, and IoT. Implementing smart sensors, drones, and data analytics can enhance real-time monitoring, enabling early pest detection and targeted interventions.

This approach improves crop yield, reduces pesticide usage, and promotes sustainable farming practices. Integration of emerging technologies will play a crucial role in shaping the future of pest detection in agriculture.

https://github.com/SunilPrasad31/PestDetection/assets/145242357/7872d874-1852-419d-b7f7-e9978a353153

REFERENCES

[1] Y. Li, C. Xia, and J. Lee. “Detection of small-sized insect pests in greenhouses based on multifractal analysis”. Oct. 2015.

[2] Y. Liu, X. Zhang, Y. Gao, T. Qu and Y. Shi. “Improved CNN Method for Crop Pest Identification Based on Transfer Learning”. Mar. 2022.

[3] Y. Hu, J. Chang, Y. Li, W. Zhang, X. Lai, and Q. Mu. “High Zoom Ratio Foveated Snapshot Hyperspectral Imaging for Fruit Pest Monitoring”. Jan. 2023.

[4] Z. Chen et al. “Study on Pear Flowers Detection Performance of YOLO-PEFL Model Trained With Synthetic Target Images”. Jun.

[5] A lightweight SSV2-YOLO based model for detection of sugarcane aphids in unstructured natural environments.

TEAM MEMBER