دانلود مقاله یادگیری عمیق برای کشاورزی دقیق
خلاصه
1. مقدمه
2. کشاورزی دقیق
3. روش شناسی
4. تجزیه و تحلیل و نتیجه
5. بحث
6. نتیجه گیری
اعلامیه منافع رقابتی
منابع
Abstract
1. Introduction
2. Precision agriculture
3. Methodology
4. Analysis and result
5. Discussion
6. Conclusion
Declaration of Competing Interest
References
چکیده
پیشرفت‌های اخیر در فن‌آوری‌های ارتباطی با ظهور اشیاء متصل، حوزه کشاورزی را تغییر داده است. در این عصر دیجیتال جدید، توسعه هوش مصنوعی، به ویژه یادگیری عمیق، امکان تسریع و بهبود در پردازش داده های جمع آوری شده را فراهم کرده است. برای برجسته کردن تکامل و پیشرفت های مشاهده شده در یادگیری عمیق در کشاورزی، ما یک مطالعه کتاب سنجی بر روی بیش از 400 مطالعه تحقیقاتی اخیر انجام دادیم. تجزیه و تحلیل های انجام شده بر روی کارهای تحقیقاتی اخیر نشان می دهد که یادگیری عمیق به طور گسترده ای در دیجیتالی کردن مناطق کشاورزی با دقت بالا بیش از تکنیک های استاندارد پردازش تصویر دخیل است. بیشتر کارها بر روی مشکلات طبقه بندی محصولات، علف های هرز و شناسایی آفات تمرکز دارند. روش های آنها عمدتا بر اساس معماری شبکه عصبی کانولوشنال است. از مطالعه موردی، ما سه چالش کلیدی را شناسایی کرده‌ایم که در روش‌های یادگیری عمیق به کار رفته در کشاورزی ضروری هستند: (1) نیاز به در نظر گرفتن درک بازیگران حوزه، تخصیص یا تعامل آنها با ابزارهای موجود. (ii) الزام به انجام آزمون‌های آماری برای تجزیه و تحلیل عملکرد طبقه‌بندی‌کننده‌های حاصل از فرآیند یادگیری؛ و (iii) نیاز به انجام اعتبار سنجی متقابل آماری با داده های آموزشی. در پایان، فرآیند پردازش داده‌های کشاورزی شامل چندین بخش را برای در نظر گرفتن بهتر انتظارات ناشی از چالش‌های مطرح شده خلاصه کردیم. ما در نظر داریم که این مطالعه می تواند به عنوان یک راهنمای تحقیق برای دانشمندان و متخصصان در کاربرد روش شناسی یادگیری عمیق در کشاورزی باشد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Recent advances in communication technologies with the emergence of connected objects have changed the agricultural area. In this new digital age, the development of artificial intelligence, particularly deep learning, has allowed for acceleration and improvement in the processing of collected data. To highlight the evolution and advances observed in deep learning in agriculture, we conducted a bibliometric study on more than 400 recent research studies. The analyses carried out on recent research works suggest that deep learning is widely involved in the digitization of agriculture areas with high accuracy exceeding the standard image processing techniques. Most of the works focus on crop classification problems, weed, and pest identification. Their methods are mainly based on convolutional neural network architecture. From the cases study, we have identified three key challenges that are essential in the deep learning methods applied in agriculture: (i) the need to consider the perception of the domain actors, their appropriation or interaction with the existing tools; (ii) the requirement to perform statistical tests to analyze the performance of the classifiers resulting from the learning process; and (iii) the need to perform statistical cross-validations with the training data. In the end, we summarized the agricultural data processing process consisting of several parts, for a better consideration of the expectations resulting from the challenges addressed. We consider that this study can serve as a guideline of research for the scientist and practician in the application of deep learning methodology in agriculture.
Introduction
Agriculture is the practice of growing food. Given the increase in population, this sector must meet many requests for food while considering societal (such as labor), environmental (water scarcity, loss of biodiversity, land degradation, etc.), and economic issues. The constraints of its development have become numerous given the seasonal variability and extreme climate. It is more necessary than ever to find innovative practices for the development of the agricultural sector.
Digital integration has significantly changed farmers' knowledge of field management with innovative technologies like intelligent computing, robotics, drones, or sensors onboard farm machinery. Using these technologies is encouraging data scientists and agronomists to design analytical tools and techniques to accurately organize field management and address the new challenges at hand (fungal attack detection, crop yield prediction, advanced spraying, etc.). These novel practices require farmers’ technical assistance to support their needs and help them maximize their crop yields based on data and task automation.
Conclusion
Precision agriculture is a recent science that is certainly still in its young phase. Deep learning can make a decisive contribution to the analysis of agricultural data, in this case by using computer vision to enable a machine to automatically analyze and understand the visual world. This offers the opportunity to develop intelligent systems, which appear to be one of the many possible ways to tackle the economic, environmental, and social challenges in agriculture. This literature review shows a richness of work in recent years that describe the development of various systems using deep learning to solve various problems in precision agriculture. The problems addressed are mainly yielded losses caused by factors affecting crop growth. In bibliometric methodology, we explored only documents from the Web of Science database. We analyzed the thematic structures and new directions of agricultural research based on deep neural networks were proposed.
Bibliometric analyses show that the most active countries are China, the United States, India, and Brazil. The hot topics covered include plant disease, feature extraction, transfer learning, disease detection, weed detection, insect pests, convolutional neural networks, image processing, remote sensing, and IoT. Convolutional neural networks (CNNs) are one of the most used architectures thanks to their successful application in deep learning. However, the use of deep learning algorithms in precision agriculture raises many challenges whose resolution is useful to enhance this field and facilitate its appropriation by the stakeholders: