This paper presents a survey of 40 research efforts that apply deep learning techniques to various agricultural and food production challenges. The authors examine the specific agricultural problems addressed, the models and frameworks used, the data sources and preprocessing methods, and the performance achieved by each study. They also compare deep learning with other existing techniques in terms of classification and regression performance. The findings indicate that deep learning provides high accuracy, outperforming traditional image processing techniques. Deep learning, a subset of machine learning, uses hierarchical data representations through convolutional and recurrent neural networks, allowing for better performance and precision. The survey highlights the growing popularity of deep learning in agriculture, with many studies published since 2015. The applications include plant recognition, fruit counting, land cover classification, and crop type identification. Data sources include satellite and aerial imagery, as well as text data. Preprocessing steps such as image resizing, segmentation, and augmentation are commonly used. Data augmentation techniques help improve model performance, especially when working with small datasets. The survey also discusses the performance metrics used, such as accuracy, F1 score, and RMSE. Deep learning models, particularly CNNs, outperform traditional methods in most cases. The paper also addresses the challenges of data variation, the importance of transfer learning, and the generalization of models across different datasets. Overall, the survey shows that deep learning is a promising technique for agricultural applications, with high accuracy and performance in various tasks.This paper presents a survey of 40 research efforts that apply deep learning techniques to various agricultural and food production challenges. The authors examine the specific agricultural problems addressed, the models and frameworks used, the data sources and preprocessing methods, and the performance achieved by each study. They also compare deep learning with other existing techniques in terms of classification and regression performance. The findings indicate that deep learning provides high accuracy, outperforming traditional image processing techniques. Deep learning, a subset of machine learning, uses hierarchical data representations through convolutional and recurrent neural networks, allowing for better performance and precision. The survey highlights the growing popularity of deep learning in agriculture, with many studies published since 2015. The applications include plant recognition, fruit counting, land cover classification, and crop type identification. Data sources include satellite and aerial imagery, as well as text data. Preprocessing steps such as image resizing, segmentation, and augmentation are commonly used. Data augmentation techniques help improve model performance, especially when working with small datasets. The survey also discusses the performance metrics used, such as accuracy, F1 score, and RMSE. Deep learning models, particularly CNNs, outperform traditional methods in most cases. The paper also addresses the challenges of data variation, the importance of transfer learning, and the generalization of models across different datasets. Overall, the survey shows that deep learning is a promising technique for agricultural applications, with high accuracy and performance in various tasks.