# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 1 Introduction  
Smart agriculture refers to the broad application of artificial intelligence (AI), which involves big data, the internet of things (IoT), deep learning, and many other digital technologies [1]. As the world population grows [2], a significant increase in food production must be realized [3]. Ensuring the constant and consistent availability and quality of food globally without affecting natural ecosystems is challenging for modern technologies. Deep learning is a new cutting-edge technology for image processing and data analysis. It has yielded promising results, possesses enormous potential, and has been successfully employed in various fields, including agriculture [4].  
In recent years, deep learning-based agricultural applications (smart agriculture) have achieved tremendous success; this concerns managing different agrain activities using data acquired from diverse sources. Various intelligent systems based on AI differ in their ability to record and interpret data and assist the farmers in making the right decisions at the right time. Data can be recorded using installed IoT nodes (sensors), processed by any deep learning method, and imposed decisions on operational areas through actuators. Other state-of-the-art technologies, such as remote sensing geographic information, global satellite positioning, and automated computer control, augment the AI system in monitoring and managing agriculture in real-time.  
Furthermore, AI-based smart agriculture can be utilized to schedule optimum resources such as fertilizer, pesticides, and water, thereby minimizing pollution and operational/ production costs and maximizing production. As AI can assist in the early detection and prevention of plant diseases, halting their spread would require the application of fewer medications; this reduces environmental contamination significantly [5]. The continuous provision of agronomic inputs, such as water, nutrients, and fertilizers, is critical for plant health, growth, and yield [6]. The absence of any of these inputs may cause biotic and abiotic stress. The decision to apply the right amount of a particular resource at the right time by considering the current situation and future predictions is possible only through AI. This research studied the utilization of AI and deep learning in agriculture and its future potential. We also investigated the agricultural parameters monitored by IoTs and used them to feed the deep learning algorithms for further processing.  
This paper presents a survey of newly developed systems in smart agriculture using deep learning techniques. The motivation for preparing this survey stems from the importance of deep learning applications and innovative solutions that deep learning techniques can provide in solving agricultural problems. The use of deep learning techniques in smart agriculture is relatively recent and in the process of progress and continuously gaining popularity in recent publications. Therefore, we have highlighted the contributions made by deep learning techniques to address the problems of smart agriculture in data processing and decision making. One of the main issues we found while surveying the recent research studies is the lack of early detection and classification of plant leaf diseases. We present a proposed system for solving this issue using a hybrid deep learning model based on a convolutional neural network and a support vector machine.  
The rest of this paper is structured as follows. The research methodology is explained in Section 2, and Section 3 briefly introduces the deep learning technology, and its applications are discussed in Section 4. Units 5 provides the technical analysis of studied papers, while detailed discussions are given in Section 6. The shortcomings found in this survey are mitigated by proposing a new hybrid system provided in Section 7. Finally, this article is concluded in Section 8.