Deep learning in agriculture: A survey
Authors: Andreas Kamilaris, Francesc X. Prenafeta-Boldu
Abstract
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
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Closest papers
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A Survey on Deep Learning Techniques in Smart Agriculture: Applications, Challenges, and Emerging TrendsD. Kataria, C. Kumar · 2025 · International Conference on Computing, Communication and Automation · Not summarized yet
Abstract
Deep learning is a cutting-edge and modern approach to image processing and data analysis. Deep learning has been one of the most influential research areas in distinct domains and has produced highly efficient results. Deep learning has lately gained popularity as a result of its success in a number of fields. Deep learning has been used for different agricultural-related jobs in recent years and has a great impact on agriculture. This article examines various deep learning methods that have been used in agriculture. In this study, an analysis of various research projects has been conducted that have been applied to numerous issues in agriculture and food production using deep learning tech…
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Deep learning and its role in smart agricultureV. S. Magomadov · 2019 · Journal of Physics, Conference Series · cited 40 times · Not summarized yet
Abstract
Deep learning is a data analysis and image-processing method, which has recently gained a lot of attention as a tool, which has great potential and promising results. There are many different fields that deep learning has been applied to and it is also being applied to the field of agriculture. The purpose of this paper is to explore deep learning in terms of agriculture and food production. The performance of deep learning in agriculture is the focus of this paper comparing it to other existing artificial intelligence models, which have been used in agriculture. In addition, several types of deep learning models are covered and their differences are explained. The paper explains why some de…
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Deep Learning Techniques Used in Agriculture: A ReviewPranita D. Sherkhane, Nayana S. Ratnaparkhi · 2024 · International Journal for Research in Applied Science and Engineering Technology · cited 1 time · Not summarized yet
Abstract
Abstract: Deep learning (DL) is a kind of sophisticated data analysis and image processing technology, with good results and great potential. DL has been applied to many different fields, and it is also being applied to the agricultural field. Deep learning algorithms have revolutionized various industries, including agriculture, by enabling advanced image processing and accurate predictions. This paper explores a wide-ranging review of research papers and articles on deep learning algorithms for image processing and predictions in the field of agriculture. It analyzed works on an overview of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, highligh…
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Deep Learning Applications in AgricultureHari Kishan Kondaveeti, Gonugunta Priyatham Brahma, Dandhibhotla Vijaya Sahithi · 2021 · Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture · cited 1 time · Not summarized yet
Abstract
Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implement…
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Abstract
Deep neural technology has a significant contribution to the invention of various systems to enhance crop farming by preventing damage caused by various crop diseases. This study explores the implementation of deep learning in the agricultural domain within some aspects: 1. Importance of deep learning in agriculture, 2. Method- ology and Aspects, and 3. Evaluation Matrices. In order to train convolution neural networks (CNN), databases are required, whereas evaluation matrices evaluate the architecture performance to check the effectiveness of the databases. This paper effectively explains all the databases and methods utilized in deep learning for crop disease identification and crop classi…
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Abstract
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Deep Learning in Agriculture: A ReviewPallab Bharman, Sabbir Ahmad Saad, Sajib Khan et al. · 2022 · Asian Journal of Research in Computer Science · cited 56 times · Not summarized yet
Abstract
Deep learning (DL) is a kind of sophisticated data analysis and image processing technology, with good results and great potential. DL has been applied to many different fields, and it is also being applied to the agricultural field. This paper presents a wide-ranging review of research with regards to how DL is applied to agriculture. The analyzed works were categorized in yield prediction, weed detection, and disease detection. The articles presented here illustrate the benefits of DL to agriculture through filtering and categorization. Farm management systems are turning into real-time AI-enabled applications that give in-depth insights and suggestions for farmer's decision support by usi…
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Abstract
The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, a…
Prior work it builds on
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Estimating crop yields with deep learning and remotely sensed dataKentaro Kuwata, R. Shibasaki · 2015 · IEEE International Geoscience and Remote Sensing Symposium · cited 212 times · Not summarized yet
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Deep Learning: Methods and ApplicationsL. Deng, Dong Yu · 2014 · Foundations and Trends® in Signal Processing · cited 3,938 times · Not summarized yet
Abstract
This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by…
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Deep machine learning provides state-of-the-art performance in image-based plant phenotypingMichael P. Pound, J. Atkinson, Alexandra J. Townsend et al. · 2016 · bioRxiv · cited 385 times · Not summarized yet
Abstract
Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.
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Deep fruit detection in orchardsSuchet Bargoti, J. Underwood · 2016 · IEEE International Conference on Robotics and Automation · cited 526 times · Not summarized yet
Abstract
An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In…
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Deep Count: Fruit Counting Based on Deep Simulated LearningM. Rahnemoonfar, C. Sheppard · 2017 · Italian National Conference on Sensors · cited 498 times · Not summarized yet
Abstract
Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits or flowers by workers is a very time consuming and expensive process and it is not pra…
Papers that cite it
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Application of Deep Learning in Food: A ReviewLei Zhou, Chu Zhang, Fei Liu et al. · 2019 · Comprehensive Reviews in Food Science and Food Safety · cited 577 times · Not summarized yet
Abstract
Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estim…
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Machine Learning and Deep Learning in Agriculture: A PRISMA Systematic Review of Architectures, Applications, and Open Science Practices (2019–2026)Azad Rasul · 2026 · Digital Intelligence in Agriculture · cited 2 times · Not summarized yet
Abstract
Agriculture faces growing pressures from food insecurity, climate change, and resource scarcity, increasing demand for scalable analytical tools. This PRISMA 2020-compliant systematic review synthesised 582 peer-reviewed studies on machine learning (ML) and deep learning (DL) in agriculture published between January 2019 and March 2026, with 430 retrieved full texts forming the analytic subsample. Publication volume increased from 6 papers in 2019 to 251 in 2025, corresponding to a compound annual growth of approximately 86%. Convolutional backbones remained dominant (57.2%), while Transformer-based models increased from 14.3% in 2022 to 41.2% in 2025. South and East Asia contributed 59.3% o…
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Deep Learning in Farming: A Systematic Evidence-Weighted Review of Applications, Validation Gaps, and Emerging FrontiersVito Domenico Amodio, Lerina Aversano, V. Dentamaro et al. · 2026 · Big Data and Cognitive Computing · Not summarized yet
Abstract
This article presents a systematic, evidence-weighted review of Deep Learning (DL) in farming, with a primary emphasis on the agricultural production stage and on four operational domains: precision crop management, precision livestock farming, soil and water resource management, and autonomous agricultural systems. Following a PRISMA 2020-oriented protocol, 56 primary studies (42 with quantitative results) were retained from an initial pool of 18,731 records. The reviewed literature reports applications in plant disease detection, weed recognition, yield prediction, fruit detection, livestock identification and health monitoring, soil-property estimation, crop-water-stress assessment, and r…
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Plant Disease Detection and Classification by Deep Learning—A ReviewLili Li, Shujuan Zhang, Bin Wang · 2021 · IEEE Access · cited 1,080 times · Not summarized yet
Abstract
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the resear…
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Deep learning – Method overview and review of use for fruit detection and yield estimationAnand Koirala, Kerry Brian Walsh, Zhenglin Wang et al. · 2019 · Computers and Electronics in Agriculture · cited 604 times · Not summarized yet
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Updated on 5 November 2026.