# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 1 Introduction  
The development of agricultural production can be broadly divided into four stages: the era of traditional agriculture, the era of mechanized agriculture, the era of automated agriculture, and the era of smart agriculture--characterized by unmanned operations and deep integration with computer science [1]. Today, the digital revolution in agriculture is gaining momentum. During this structural transformation, the agricultural sector faces severe labor shortages due to a declining workforce and rural-to-urban migration, while societal demand for agricultural output continues to rise. This imbalance between labor scarcity and growing demand threatens agricultural productivity and sustainability [2]. From a global perspective, as challenges related to food security and resource sustainability intensify, the need to reform agricultural practices and enhance productivity has become more urgent than ever. Pressing issues such as population growth, climate change, and urbanization--compounded by dwindling natural resources--cast a shadow over the future [3]. Agricultural progress plays a vital role in a nation's economy and survival [4]. As noted in [5], achieving sustainable agriculture requires improving production efficiency, adopting precision farming, and reducing waste on the demand side. Therefore, technological innovation and agricultural transformation are imperative. Traditionally, acquiring agricultural data was labor-intensive, time-consuming, and prone to errors. However, with the integration of computer science and agriculture, technologies such as remote sensing, digital applications, sensors, hyperspectral imaging, and artificial intelligence have ushered agriculture into an era of intelligent transformation [6]. Computer vision technology plays a pivotal role in this agricultural revolution. Thanks to its advancements, it has been widely adopted in smart agriculture [7]. Deep learning, a field closely related to computer vision, enables automated feature extraction and learning from large-scale datasets, achieving unprecedented capabilities in image understanding [8]. It demonstrates state-of-the-art performance in video analysis, facial recognition, image classification, biomedical applications, and health informatics. Moreover, its exceptional data processing and learning capabilities are proving indispensable in agriculture [9]. Agricultural production is a complex and systematic industry involving a complete workflow from pre-planting preparation to post-harvest handling. This paper categorizes the agricultural production process into three key phases as follows. The first phase is preparation, which requires comprehensive land assessment, soil health evaluation, and crop suitability analysis [10; 11]. Modern soil testing has evolved into an intelligent system integrating spectroscopy, electrochemistry, molecular biology, and other technologies, significantly improving production efficiency and serving as a critical component in agriculture [12]. The second phase is crop growth management. This stage encompasses seed selection, growth monitoring, grading and classification, disease detection, and weed identification in fields. It directly determines crop yield, quality, and economic returns. The integration of computer vision and deep learning in this phase maximizes the potential of land, seeds, water, fertilizers, and other resources while mitigating risks [13; 14; 15; 16]. The final phase is harvesting and post-harvest handling. Crop growth environments are complex and seasonal, with harvest periods often concentrated within short timeframes. The methods of harvesting and processing directly affect product quality, shelf life, and market value. Against the backdrop of labor shortages and surging demand for crop yields, the transition toward automation and intelligent solutions has become an inevitable trend [17; 18; 19]. The rapid advancement of computer vision and deep learning has highlighted their pivotal role in agriculture.