Despite the perception people may have regarding the agricultural process, the reality is that today's agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges.

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Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk

Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China.

Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance

Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth’s surfaces. More specifically, current SITS combine high temporal, spectral and spatial resolutions, which makes it possible to closely monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied to create land cover maps from SITS, these algorithms do not make the most of the temporal domain. This paper proposes a comprehensive study of Temporal Convolutional Neural Networks (TempCNNs), a deep learning approach which applies convolutions in the temporal dimension in order to automatically learn temporal (and spectral) features. The goal of this paper is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification, as compared to RF and Recurrent Neural Networks (RNNs) —a standard deep learning approach that is particularly suited to temporal data. We carry out experiments on Formosat-2 scene with 46 images and one million labelled time series. The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification. We provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size; we also draw out some differences with standard results in computer vision (e.g., about pooling layers). Finally, we assess the visual quality of the land cover maps produced by TempCNNs.

Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images - eScholarship

This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.

https://www.mdpi.com/2072-4292/10/1/75/pdf

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

Global maps of land cover and leaf area index (LAI) from the moderate resolution imaging spectroradiometer (MODIS) are available from the earth resources observation system data center. Validation of these MODIS terrestrial products is crucial for scientific research. Numerous pre- and postlaunch validation activities have been completed during the last several years. However, such work was mainly located in North America, Europe, and Africa. The validated vegetation types were principally focused on woodland and savanna. In this paper, we evaluated the Collection 3 MODIS land cover (MOD12Q1) and the Collection 4 MODIS LAI (MOD15A2) products in the North China Plain (NCP). We placed the emphasis on croplands by: (1) developing an accurate land cover map, based on the MODIS LAI/fraction of photosynthetically active radiation land cover classification scheme; (2) deriving empirical LAI validation maps, based on in situ measurement and landsat thematic mapper imagery; and (3) conducting an initial MODIS validation exercise to assess the quality of MODIS products. The results indicate that an apparent misclassification exists between grasses/cereal crop and broadleaf crop biomes in the MODIS land cover product. Although MODIS LAI could resemble the general pattern of the LAI seasonal vegetation variation in the NCP, the MODIS LAI algorithm markedly underestimated LAI values (down to 2-3 m2/m2) for the April 2004 winter wheat cropland. Biome misclassification and the atmospheric effect of cloud or aerosol were proven to decrease the value of MODIS LAI, but these impacts cannot fully explain the significant underestimation. Additional studies are needed to understand the main reasons for this underestimation in the study area.

Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images

Accurate localization of moving sensors is essential for many fields, such as robot navigation and urban mapping. In this paper, we present a framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous sensor model in a panoramic camera. The rigorous model does not cause system errors, thus representing an improvement over the widely used ideal sensor model. The proposed SLAM does not require additional restrictions, such as loop closing, or additional sensors, such as expensive inertial measurement units. In this paper, the problems of the ideal sensor model for a panoramic camera are analysed, and a rigorous sensor model is established. GPS data are then introduced for global optimization and georeferencing. Using the rigorous sensor model with the geometric observation equations of BA, a GPS-supported BA-SLAM approach that combines ray observations and GPS observations is then established. Finally, our method is applied to a set of vehicle-borne panoramic images captured from a campus environment, and several ground control points (GCP) are used to check the localization accuracy. The results demonstrated that our method can reach an accuracy of several centimetres.

https://www.mdpi.com/1424-8220/13/1/119/pdf

GPS-supported visual SLAM with a rigorous sensor model for a panoramic camera in outdoor environments.

Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human-land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.

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Yun Shi

Papers

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images

GPS-supported visual SLAM with a rigorous sensor model for a panoramic camera in outdoor environments.

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

eFarm: A Tool for Better Observing Agricultural Land Systems.