مروری بر پیش‌بینی عملکرد محصول با استفاده از الگوریتم‌های هوش مصنوعی

نویسندگان

1 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران.

2 گروه مهندسی کامپیوتر - دانشکده مهندسی برق و کامپیوتر - دانشگاه تبریز - تبریز- ایران

3 گروه مهندسی برق- دانشکده مهندسی برق و کامپیوتر - دانشگاه تبریز - تبریز- ایران

چکیده

هوش مصنوعی در صنایع مختلف به‌ویژه صنعت کشاورزی کاربردهای گسترده‌ای دارد که به طور چشمگیری به افزایش بهره‌وری، کاهش هزینه‌ها و بهبود خدمات کمک می‌کند. این مقاله مروری جامع بر تحقیقات اخیر در زمینه پیش‌بینی عملکرد محصول با استفاده از الگوریتم‌های هوش مصنوعی ارائه می‌دهد. طیف گسترده‌ای از روش‌های یادگیری ماشین، ابزارها، داده‌کاوی، چالش‌ها و محدودیت‌های موجود و مجموعه ‌داده‌هایی را که برای پیش‌بینی عملکرد محصولات مختلف استفاده شده‌اند، مورد بررسی قرار گرفته است. بررسی‌ها با تمرکز بر طیف گسترده‌ای از محصولات، از جمله برنج، گندم، نیشکر و سویا بر اهمیت پیش‌بینی عملکرد محصول در کشاورزی دقیق و تصمیم‌گیری کشاورزان تأکید می‌کند. استفاده از الگوریتم‌های یادگیری ماشین شامل روش‌های نظارت‌شده و نظارت‌نشده، مانند رگرسیون، درخت‌های تصمیم‌گیری، ماشین‌های بردار پشتیبان و مدل‌های عمیق مانند شبکه‌های عصبی مصنوعی، در این تحقیقات مورد بحث قرار گرفته‌اند. ابزارهای متعددی مانند TensorFlow، Keras و Scikit learn برای توسعه و آزمایش این مدل‌ها به کار گرفته شده‌اند. داده‌کاوی به استخراج الگوهای معنی‌دار از داده‌های وسیع کشاورزی کمک می‌کند. همچنین، چالش‌ها و محدودیت‌های موجود مانند کیفیت داده‌ها، تفسیر مدل‌ها و نیاز به تطبیق با شرایط خاص حوزه کشاورزی نیز مورد بررسی قرار گرفته‌اند. با مقایسه معیارهای عملکرد مدل‌‌‌های مختلف یادگیری ماشین، شبکه‌‌‌های عصبی مصنوعی، جنگل تصادفی و مدل‌‌‌های پیش‌بینی مبتنی بر ماشین‌بردار پشتیبان برای پیش‌‌‌بینی عملکرد محصول مناسب‌‌‌تر هستند و دقت بالایی دارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

An Overview of Product Performance Prediction Using Artificial Algorithms

نویسندگان [English]

  • Adel Taherihajivand 1
  • kimia shirini 2
  • sina samadi gharehveran 3
1 Department of Biosystems, Faculty of Agricultural, University of Tabriz, Tabriz, Iran
2 Tabriz
3 Doctoral student of electrical engineering at Tabriz University
چکیده [English]

Introduction
Artificial intelligence (AI) plays an essential role in enhancing productivity, reducing costs, and improving service delivery across various sectors, especially in agriculture. This paper offers a comprehensive review of recent advancements in AI applications for predicting agricultural product performance. Emphasizing the potential of AI, specifically machine learning (ML) algorithms, in precision agriculture, the study highlights its impact on crop yield prediction for crops such as rice, wheat, sugarcane, and soybean. The paper explores the benefits that AI brings to farmers' decision-making by enabling accurate yield predictions, addressing the urgent need for optimized resource utilization, and meeting increasing food demands.
Materials and Methods
The study focuses on the application of various machine learning algorithms, including supervised and unsupervised learning methods, to predict agricultural product performance. Algorithms such as regression, decision trees, support vector machines, and advanced models like artificial neural networks (ANNs) were analyzed. Several tools, including TensorFlow, Keras, and Scikit-Learn, were employed for model development and testing. These tools facilitated data handling and modeling processes, enabling the extraction of significant patterns from large agricultural datasets through data mining. This approach offers insights into critical factors affecting crop yields and helps refine AI models for more accurate predictions.
Results and Discussion
The performance of multiple machine learning algorithms was assessed by evaluating their ability to predict crop yield across various crops. Artificial neural networks, random forest, and support vector machine models demonstrated the highest accuracy in predicting crop performance, making them particularly suited for applications in precision agriculture. Despite the promising results, challenges remain, such as ensuring high-quality data, improving model interpretability, and adapting algorithms to specific agricultural contexts. Addressing these challenges can enhance the models’ practical application in real-world scenarios, allowing farmers to make more informed decisions based on precise yield forecasts.
 
Conclusion
This review underscores the effectiveness of AI-based models, particularly ANNs, random forests, and support vector machines, in predicting agricultural yield with high accuracy. By addressing limitations in data quality, model interpretability, and environmental adaptation, AI models have the potential to revolutionize agriculture, enabling farmers to manage resources more effectively and make data-driven decisions to maximize crop yields. The ongoing improvement of AI tools and techniques is essential for addressing the challenges in precision agriculture and meeting the global food demands of the future.
Acknowledgement   
The authors would like to express their gratitude to the University of Tabriz for providing the resources needed to conduct this research.

کلیدواژه‌ها [English]

  • Algorithms
  • Artificial Intelligence
  • Performance
  • Product
  • Machine Learning
  • Sugarcane

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مکانیزاسیون کشاورزی
دوره 9، شماره 3
مهر 1403
صفحه 1-14

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  • تاریخ دریافت: 11 خرداد 1403
  • تاریخ بازنگری: 01 آبان 1403
  • تاریخ پذیرش: 02 آبان 1403

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