Secure Authentication Scheme Using Diffie–Hellman Key Agreement for Smart IoT Irrigation Systems

Smart irrigation is considered one of the most significant agriculture management systems worldwide, considering the current context of water scarcity. There is a clear consensus that such smart systems will play an essential role in achieving the economic growth of other vital sectors. In general, the consequences of global warming and the unavailability of clean water sources for the agricultural sector are clear indications that the demand for these systems will increase in the near future, especially considering the recent expansions in the use of the Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies, which have been employed in the development of such systems. An obvious result is that security challenges will be one of the main obstacles to attaining the widespread adoption of such systems. Therefore, this paper proposes a secure authentication scheme using Diffie–Hellman key agreement for smart IoT irrigation systems using WSNs. This scheme is based on Diffie–Hellman and one-way hash cryptographic functions in order to support the basic security services with a high data rate and ability to resist well-known attacks. The Burrows–Abadi–Needham (BAN) logic model is used to verify the proposed scheme formally. Based on various possible attack scenarios, a resistance analysis of the proposed scheme is discussed. Further analyses are performed in terms of the storage size, intercommunication, and running time costs. Therefore, the proposed scheme not only can be considered a secure authentication scheme but is also practical for smart IoT irrigation systems due to its reasonable efficiency factors.

Essentials, Challenges, and Future Directions of Agricultural IoT

Agriculture plays a major role in the socio-economic structure of India. A recent report claimed that population of India is increasing faster than its capability to produce rice, wheat, and vegetables. The challenges in the area of agriculture are farming, watering, weather forecasting, marketing, and transportation. These challenges are to be addressed towards proper solution. If the infrastructure and productivity of the food increases, then India can easily feed its population as well as improve the exports of wheat and rice around the world. Internet of things (IoT) is an emerging technical area of agriculture domain. The advantage of IoT is to implement a smart agriculture management system with the help of analyzing the weather conditions of the field in order to optimize the usage of water, energy, fertilizers so as to maximize the crop yield. The objective of this study is to explore the possible contributions of IoT in Indian agriculture towards the improvements in irrigation infrastructure, agricultural productivity, food security, and rural job opportunities.

Mechanical Durability Assessment of an Energy-Harvesting Piezoelectric Inverted Flag

This paper presents results from a practical assessment of the endurance of an inverted flag energy harvester, tested over multiple days in a wind tunnel to provide first insights into flapping fatigue and failure. The inverted flag is a composite bimorph, composed of PVDF (polyvinylidene difluoride) strips combined with a passive metallic core to provide sufficient stiffness. The flag, derived from an earlier, more extensive study, flaps with a typical amplitude of ~120 degrees and a frequency of ~2 Hz, generating a constant power of ~0.09 mW in a wind velocity of 6 m/s. The flag was observed to complete ~5×105 cycles before failure, corresponding to ~70 h of operation. The energy generated over this lifespan is estimated to be sufficient to power a standard low-power temperature sensor for several months at a sampling rate of one sample/minute, which would be adequate for applications such as wildfire detection, environmental monitoring, and agriculture management. This study indicates that structural fatigue may present a practical obstacle to the wider development of this technology, particularly in the context of their usual justification as a ‘deploy and forget’ alternative to battery power. Further work is required to improve the fatigue resistance of the flag material.

Automated Vigor Estimation on Vineyards

Estimating the balance or vigor in vines, as the yield to pruning weight relation, is a useful parameter that growers use to better prepare for the harvest season and to establish precision agriculture management of the vineyard, achieving specific site planification like pruning, debriefing or budding. Traditionally growers obtain this parameter by first manually weighting the pruned canes during the vineyard dormant season (no leaves); second during the harvest collect the weight of the fruit for the vines evaluated in the first step and then correlate the two measures. Since this is a very manual and time-consuming task, growers usually obtain this number by just taking a couple of samples and extrapolating this value to the entire vineyard, losing all the variability present in theirs fields, which imply loss in information that can lead to specific site management and consequently grape quality and quantity improvement. In this paper we develop a computer vision-based algorithm that is robust to differences in trellis system, varieties and light conditions; to automatically estimate the pruning weight and consequently the variability of vigor inside the lot. The results will be used to improve the way local growers plan the annual winter pruning, advancing in the transformation to precision agriculture. Our proposed solution doesn\textsc{\char13}t require to weight the shoots (also called canes), creating prescription maps (detail instructions for pruning, harvest and other management decisions specific for the location) based in the estimated vigor automatically. Our solution uses Deep Learning (DL) techniques to get the segmentation of the vine trees directly from the image captured on the field during dormant season

Climate change and agriculture management: Western Balkan region analysis

Background This paper aims to analyze the possibilities of the agricultural sector of the Western Balkan to assess compliance with the European Green Deal, which provides for the implementation of activities, which should enable the transition to sustainable agriculture and climate change mitigation. This paper is among the first to present the causality of agriculture and climate change (status, mitigation, and perspectives) in general and in light of the European Green Deal for the Western Balkan territory. Main text Agricultural production is a leading industry in the Western Balkan. Climate change and predictions that temperatures will increase by 4 °C in the coming decades pose a risk not only to agricultural production but also to the safety of the population, because agriculture is the main source of income for a significant part of it. Uncontrolled floods and droughts caused by climate change are a particular danger for agriculture and human existence. This paper demonstrates that agriculture in the WB can be considered critically affected by climate change. Conclusions Unless appropriate measures are taken and risk management for water resources and agriculture is improved, there will be a further decrease in precipitation and an increase in dry days by 20%. Such a scenario endangers not only the already vulnerable climate sustainability and biodiversity of the region but also the existence of a population employed in agriculture and the contribution of the agricultural sector to the gross domestic product. However, future planning based on the Common Agriculture Policy (CAP) and European Green Deal, the adoption of a related regulatory framework, the establishment and regular monitoring of supporting financing mechanisms, regional cooperation, and improving risk management (with emphasis on the local level) can mitigate the present impact and decrease the expected negative impact of climate change on agriculture and biodiversity in the WB region.

Farm-n-Pedia: Expert mobile agricultural knowledge-based system for Indian Farmers

Efficiency in farming productivity and optimum utilization of resources in the agriculture sector in developing countries is a challenge that can be addressed with technological advancement. There is also a strong need to work on the farming community’s engagement to make better farming decisions. This study aims to create an expert knowledge-based system (KBS) in a mobile application to help Indian farmers improve their agricultural practices and increase crop productivity. A prototype mobile application, ‘Farm-n-Pedia,’ is designed and used to fulfill the farmers’ informational and engagement needs. It provides a tool for agriculture management using a single platform. The expert KBS incorporates a crowdsourcing system as part of the knowledge base and interface design. The mobile application enables the users to access the worldwide information they want, get personalized expert guidance, interact with the local agrarian community, know about the latest farming techniques and technology, crowdsource data collection and increase agricultural productivity.

Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series

Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly mapped. In this study, a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture, namely crop rotation mapping (CRM), were proposed to synergize the synthetic aperture radar (SAR) and optical time series in a rotational mapping task. The proposed end-to-end architecture had reasonable accuracies (i.e., accuracy > 0.85) in mapping crop rotation, which outperformed other state-of-the-art non-deep or deep-learning solutions. For some confusing rotation types, such as fallow-single rice and crayfish-single rice, CRM showed substantial improvements from traditional methods. Furthermore, the deeply synergistic SAR-optical, time-series data, with a corresponding attention mechanism, were effective in extracting crop rotation features, with an overall gain of accuracy of four points compared with ablation models. Therefore, our proposed method added wisdom to dynamic crop rotation mapping and yields important information for the agro-ecosystem management of the study area.

A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

Singapore vs. the ‘Singapore of Africa’—Different Approaches to Managing Urban Agriculture

Through structured comparison, this article seeks to present the different approaches to urban agriculture in the cities of Singapore and Kigali. The former is seen as a model ‘smart city’ worth following worldwide, while the latter is frequently referred to as the ‘Singapore of Africa’. The research conducted was divided into two stages. The first one was desk-based and included the analysis of satellite and aerial images along with the analysis of legal documents regarding land ownership and urban agriculture management. The second one was based on field work carried out in 2019 in both cities and comprised the mapping of areas encompassed by urban agriculture, the collection of photographic documentation, field observations, as well as semi-structured interviews. The research was summarized in line with a comparative analysis of institutional and legal framework of urban agriculture and policy towards its development; spatial features of urban agriculture, including distribution, location, and area; as well as inherent features of urban agriculture, including systems of production, main crops, production methods, and functions. The process makes it clear that despite the fact that urban agriculture is considered in planning documents of both cities, the scale of the activity and the approach towards it differ markedly. In Singapore, the authorities support mainly the high-technology and land-efficient solutions, with other, low-profit forms of agricultural activity being pushed out from the urban space. In turn, in Kigali, where the scale of agricultural activity is incomparably greater, the inhabitants enjoy a certain freedom to make use of unused land in cultivation, which increases their food security and enhances their ability to cope with external stresses.