Deep learning in plant phenological research: A systematic literature review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field

A survey of image-based computational learning techniques for frost detection in plants

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring

Plant recognition, detection, and counting with deep learning

In agricultural and farm management, plant recognition, plant detection, and plant counting systems are crucial. We can apply these tasks to several applications, for example, plant disease detection, weed detection, fruit harvest system, and plant species identification. Plants can be identified by looking at their most discriminating parts, such as a leaf, fruit, flower, bark, and the overall plant, by considering attributes as shape, size, or color. However, the identification of plant species from field observation can be complicated, time-consuming, and requires specialized expertise. Computer vision and machine-learning techniques have become ubiquitous and are invaluable to overcome problems with plant recognition in research. Although these techniques have been of great help, image-based plant recognition is still a challenge. There are several obstacles, such as considerable species diversity, intra-class dissimilarity, inter-class similarity, and blurred resource images. Recently, the emerging of deep learning has brought substantial advances in image classification. Deep learning architectures can learn from images and notably increase their predictive accuracy. This thesis provides various techniques, including data augmentation and classification schemes, to improve plant recognition, plant detection, and plant counting system

Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community

Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on Geoscience and Remote Sensing (TGRS), in press, 201

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

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.This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.info:eu-repo/semantics/publishedVersio