Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Zhu, Nanyang; Liu, Xu; Liu, Ziqian; Hu, Kai; Yingkuan, Wang; Tan, Jinglu; Huang, Min; Zhu, Qihong; Ji, Xunsheng; Jiang, Yongnian; Guo, Yufeng

doi:10.25165/j.ijabe.20181104.4475

Cited by 119 publications

(53 citation statements)

References 51 publications

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“…For example, Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) are very useful in processing time-series data, which are typically used in agriculture for time prediction. Generative Adversarial Network (GAN) can be used to enrich datasets and it has been applied in agriculture [34,35], but it has not been widely used in agricultural dense scenes. In addition, Figure 4 shows the uses of surveyed DL methods in a recent study of dense scenes in agriculture.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

115

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Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

115

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“…A deep neural model with many parameters can be used for crop classification, yield prediction, and early detection of stress and disease. A considerable amount of computer vision-based work in smart agriculture focuses on plant stress detection, either as disease early detection [63] or water stress detection [64][65][66][67][68].…”

Section: Big Data Collection and Deep Learningmentioning

confidence: 99%

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Cafuta

Dodig

Cesar

et al. 2021

Sensors

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Multidisciplinary approaches in science are still rare, especially in completely different fields such as agronomy science and computer science. We aim to create a state-of-the-art floating ebb and flow system greenhouse that can be used in future scientific experiments. The objective is to create a self-sufficient greenhouse with sensors, cloud connectivity, and artificial intelligence for real-time data processing and decision making. We investigated various approaches and proposed an optimal solution that can be used in much future research on plant growth in floating ebb and flow systems. A novel microclimate pocket-detection solution is proposed using an automatically guided suspended platform sensor system. Furthermore, we propose a methodology for replacing sensor data knowledge with artificial intelligence for plant health estimation. Plant health estimation allows longer ebb periods and increases the nutrient level in the final product. With intelligent design and the use of artificial intelligence algorithms, we will reduce the cost of plant research and increase the usability and reliability of research data. Thus, our newly developed greenhouse would be more suitable for plant growth research and production.

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“…In summary, the deep learning applications deliver many opportunities in the field agriculture [12]:…”

Section: Related Workmentioning

confidence: 99%

Deep neural networks with transfer learning in millet crop images

Coulibaly

Kamsu-Foguem

Kamissoko³

et al. 2019

Computers in Industry

209

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Plant or crop diseases are important items in the reduction of quality and quantity in agriculture. Therefore, the detection and diagnosis of these diseases are very necessary. The appropria te classification with smalt datasets in Deep Learning is a major scientific challenge. Furthermore, it is difficult and expensive to generate labeled data manually according to certain selection criteria. The approaches using transfer learning aims to resolve this problem by recognizing and applying knowledge and abilities learned in previous tasks to nove! tasks (in new domains). Convolutional neural network Feature extraction Classification Precision agriculture Mildew disease In this paper, we propose an approach using transfer learning with feature extraction to build an identification system of mildew disease in pearl millet. The deep learning facilita tes a practically fast and interesting data analysis in precision agriculture. The expected advantage of the proposai is to provide support to stakeholders (researchers and farmers) through the information and knowledge generated by the reasoning process. The experimental result gives an encouraging performance that is the accuracy of 95.00%, the precision of 90.50%, the recall of 94.50% and the fl score of 91.75%.

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Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Cited by 119 publications

References 51 publications

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Deep neural networks with transfer learning in millet crop images

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