Abstract
Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.
Highlights
European landscapes have been transformed primarily by human activities, so that pristine vegetation and real wilderness are currently extremely rare in the European Union (EU)
Ten articles were related to plant diseases, eleven articles were related to fruit detection, five articles were related to weed detection, six articles were related to species detection, three articles were related to soil management, five articles were related to water management, and six articles were related to automation in agriculture
The results indicated that the changes in the visual appearance of the images in the training and test dataset could lead to a decrease in model performance, and adding additional information, such as vegetation indices, leads to better generalization for other fields
Summary
European landscapes have been transformed primarily by human activities, so that pristine vegetation and real wilderness are currently extremely rare in the European Union (EU). We are already exceeding the Earth’s capacity with the current type of agricultural production. Degradation of soils, pollution, rising costs of groundwater and pump irrigation, the transition from a fuel-based to a bio-based economy, the scarcity of freshwater as demand increases, and other environmental and economic issues will make access to fresh food an ever-greater challenge [2,3]. It has already been shown that some of the aforementioned practices such as climate changes and scarcity of freshwater lead to stagnation and sometimes even a decline in production. The question arises whether transforming all agricultural systems into high-intensity farming systems alone is not counterproductive in an attempt
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
