Deep learning in agriculture: A survey

Deep learning has emerged as a powerful technique for image processing and data analysis, showing promising results and great potential in various domains. Recently, it has also made its way into the field of agriculture. In this paper, the authors conduct a comprehensive survey of 40 research efforts that utilize deep learning techniques to address agricultural and food production challenges. The authors examine the specific agricultural problems studied in each research effort, the models and frameworks employed, the sources and nature of data used, as well as the preprocessing techniques applied. They also evaluate the overall performance achieved by each work based on specific metrics. Additionally, comparisons between deep learning and other popular techniques are analyzed in terms of classification or regression performance. One key aspect explored is the data sources used to train deep learning models. The majority of papers rely on large datasets consisting of thousands of images. These datasets can either be real images collected by researchers or synthetic ones generated specifically for their studies. Well-known publicly available datasets such as PlantVillage, LifeCLEF, MalayaKew, UC Merced, and Flavia are commonly utilized. However, some papers dealing with land cover classification, crop type identification, yield estimation, and weed detection employ smaller sets of tens or hundreds of images obtained from UAVs (Unmanned Aerial Vehicles), airborne platforms, satellite-based remote sensing systems or even X-ray tomography. The complexity of the problem being addressed often determines the amount of data required for training deep learning models. For instance, problems involving a large number of classes or small variations among classes necessitate a larger number of input images to achieve accurate results. Furthermore, the authors investigate whether researchers compared their deep learning approach with other techniques and if there were any differences in performance observed. The detailed findings are presented in Section 4 and can be found in Appendix II. Deep learning extends traditional machine learning methods by incorporating more complexity into models and employing various functions to transform data hierarchically. This allows for automatic feature extraction from raw data without manual intervention which leads to higher accuracy than conventional image processing techniques due to its ability to parallelize computations effectively.. Overall ,the findings from this survey indicate that deep learning offers high accuracy when addressing various agricultural challenges providing valuable insights for future research in this field .

Deep Learning in Agriculture: A Comprehensive Survey

In recent years, deep learning has emerged as a powerful technique for image processing and data analysis. It has been applied to various domains with promising results and great potential. Recently, it has also made its way into the field of agriculture. This paper presents a comprehensive survey of 40 research efforts that utilize deep learning techniques to address agricultural and food production challenges.

Research Efforts Examined

The authors examine the specific agricultural problems studied in each research effort, the models and frameworks employed, the sources and nature of data used, as well as the preprocessing techniques applied. They also evaluate the overall performance achieved by each work based on specific metrics such as accuracy or F1 score. Additionally, comparisons between deep learning and other popular techniques are analyzed in terms of classification or regression performance.

Data Sources Used

One key aspect explored is the data sources used to train deep learning models. The majority of papers rely on large datasets consisting of thousands of images which can either be real images collected by researchers or synthetic ones generated specifically for their studies. Well-known publicly available datasets such as PlantVillage, LifeCLEF, MalayaKew, UC Merced, and Flavia are commonly utilized. However, some papers dealing with land cover classification, crop type identification, yield estimation, and weed detection employ smaller sets of tens or hundreds of images obtained from UAVs (Unmanned Aerial Vehicles), airborne platforms, satellite-based remote sensing systems or even X-ray tomography. The complexity of the problem being addressed often determines the amount of data required for training deep learning models; problems involving a large number of classes or small variations among classes necessitate a larger number of input images to achieve accurate results.

Comparison With Other Techniques

The authors investigate whether researchers compared their deep learning approach with other techniques and if there were any differences in performance observed . Detailed findings are presented in Section 4 (Appendix II) which indicate that deep learning offers high accuracy when addressing various agricultural challenges providing valuable insights for future research in this field .

Conclusion

Deep learning extends traditional machine learning methods by incorporating more complexity into models and employing various functions to transform data hierarchically without manual intervention leading to higher accuracy than conventional image processing techniques due to its ability to parallelize computations effectively.. This survey provides an overview on how different research efforts have leveraged this technology towards solving various agricultural problems while highlighting both advantages over traditional methods along with areas where further improvement is needed .