Main Article Content
In this study, we have explored recent advancements in applying deep learning (DL) techniqueswithin theagricultural sector. By reviewing studies published from 2015to 2022, the researchsheds light on the diverse applications of DL in agriculture. These applications encompass taskslike fruit counting, water management, crop, and soil management, weed detection, seedclassification, yield prediction, disease identification, and harvesting. The study underscoresthe potential of DL in revolutionizing agriculture, leveraging its ability to learn from extensivedatasets. However, challenges such as data compilation, computational costs, and the scarcityof DL experts exist.Here, we aimto mitigate these challenges by presenting this survey as avaluable resource. This resource aims to guide future research and development endeavours
focused on integrating DL techniques in agricultural practices.