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.

/pdf/internet-of-things-iot-based-smart-agriculture-toward-making-5eeb38hwv6.pdf

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

Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated. 

/pdf/smart-farming-internet-of-things-iot-based-sustainable-1zhlg9gd.pdf

Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture

The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture.

https://www.mdpi.com/2071-1050/13/9/4883/pdf

Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms. 

/pdf/deep-learning-techniques-to-classify-agricultural-crops-1u0lkw4u.pdf

Deep learning techniques to classify agricultural crops through UAV imagery: a review

The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R2=0.7) using integrated sensors when an upper bound of 400  Mg ha−1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density.

/pdf/above-ground-biomass-prediction-by-sentinel-1-multitemporal-1yyq225nfk.pdf

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

https://link.springer.com/content/pdf/10.1186%2Fs40663-015-0035-6.pdf

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

Landscape disturbances such as forest harvest, blowdowns, fire, and development activities create patches on the landscape that modify the structure and integrity of ecosystems. Understanding the agents of change, where they occur, and how much of the landscape they are affecting will assist resource managers in making difficult decisions. To fulfill this goal, the National Park Service implemented a long-term monitoring program to quantify landscape dynamics across 1.5 million ha within and adjacent to eight national parks in the Upper Midwest United States using an automated satellite-based change detection program called LandTrendr (Landsat-based detection of trends in disturbance and recovery). The disturbance agents detected inside parks included beaver, blowdown, development, fire, flooding, insect/disease, and forest harvest. Pictured Rocks National Lakeshore had the largest percentage of area affected inside a park (11.83%, 1.96% per yr), and Isle Royal National Park had the lowest percentage of land affected (0.03%, 0.05% per yr). Tree defoliation due to insect/disease affected the largest percentage of land inside parks (1.56%, or 0.26% per yr) but did not result in tree mortality. Adjacent to parks, disturbance agents detected included agriculture, beaver, blowdown, development, insect/disease, fire, flooding, and forest harvest. Lands adjacent to Pictured Rocks National Lakeshore experienced the highest rate of disturbance (8.75%, 1.45% per yr), largely due to forest harvests. The lands adjacent to the Mississippi National River and Recreation Area experienced the lowest percentage of change (0.62%, 0.10% per yr), with development activities being responsible for most of the change. Forest harvesting was the major change agent outside six of the eight parks, an indication of how important the wood products industry is in the region and the level to which this region is forested. These national parks are acting as integral buffers from adjacent lands that either do not have the ability or lack the capacity to allow the ecosystem to function without intensive management efforts.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.1467

Are U.S. national parks in the Upper Midwest acting as refugia? Inside vs. outside park disturbance regimes

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Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

Are U.S. national parks in the Upper Midwest acting as refugia? Inside vs. outside park disturbance regimes