Securing Smart Agriculture with Communication-Efficient Federated Unlearning

Agriculture has always been a crucial part of the economy, but urbanization has started to reduce our fertile land. This rapid reduction has increased the infestations and diseases, bottlenecking production rates and threatening food safety. However, recent advancements in the IoT have posed many potential solutions for this emerging problem. Incorporating IoT-based infrastructures like a smart-farming system can reduce production costs and increase productivity and overall quality while maintaining environmental balance. This study aims to create a smart agriculture system using IoT that can maintain suitable harvesting conditions and classify diseases through a transfer learning-based approach using Deep Convolutional Neural Network (DCNN). Thereon necessary suggestions are passed to the user through the Android app. The system can successfully detect and suggest diagnosis to six classes, including healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight, with an average accuracy of 96.08%. The precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616 using the non-normalized augmented dataset. The work is expected to offer a roadmap for the future generation of Smart Agriculture Systems by summarizing the cultivation system's disease detection, prevention, and overall optimization process.

Smart agriculture is a new concern in IoT systems. IoT technologies can provide information about the agriculture field and its surroundings and act accordingly. Architecting smart agriculture systems can benefit from architecting methods available for IoT systems. In this paper, we introduce CSA-CAPS, a Climate Smart Agriculture modeling framework designed based on CAPS. CAPS is an architecture-driven modeling framework for developing IoT Systems. CSA-CAPS includes a modified meta model for the software architecture of CAPS. It adds special sensors and functionalities for climate concerns of building smart agriculture systems. This paper shows the capability of CSA-CAPS by applying motivating scenarios.

Smart agriculture using IoT for automated irrigation, water and energy efficiency

Parameter sensitivity analysis can determine the influence of the input parameters on the model output. Identification and calibration of critical parameters are the crucial points of the process model optimization. Based on the Extended Fourier Amplitude Sensitivity Test (EFAST) and the Morris method, this paper analyzes and compares the parameter sensitivity of the annual mean net primary productivity (NPP) of Larix olgensis Henry forests in Jilin Province simulated by the Lund–Potsdam–Jena dynamic global vegetation model (LPJ model) in 2009–2014 and 2000–2019, and deeply examines the sensitivity and influence of the two methods to each parameter and their respective influence on the model’s output. Moreover, it optimizes some selected parameters and re-simulates the NPP of Larix olgensis forests in Jilin Province from 2010 to 2019. The conclusions are the following: (1) For the LPJ model, the sensitive and non-influential parameters could be identified, which could guide the optimization order of the model and was valuable for model area applications. (2) The results of the two methods were similar but not identical. The sensitivity parameters were significantly correlated (p < 0.05); parameter krp was the most sensitive parameter, followed by parameters αm, αa and gm. These sensitive parameters were mainly found in the photosynthesis, water balance, and allometric growth modules. (3) The EFAST method had a higher precision than the Morris method, which could calculate quantitatively the contribution rate of each parameter to the variances of the model results; however, the Morris method involved fewer model running times and higher efficiency. (4) The mean relative error (MRE) and mean absolute error (MAE) of the simulated value of LPJ model after parameter optimization decreases. The optimized annual mean value of NPP from 2010 to 2019 was 580 g C m−2 a−1, with a mean annual growth rate of 2.13%, exhibiting a fluctuating growth trend. The MAE of the simulated value of LPJ model after parameter optimization decreases.

In modern agriculture, challenges along with aid optimization, climate trade model, and the growing demand for food production require modern solutions. Farmers, in particular those working in arid regions including Saudi Arabia, encounter sizeable demanding situations in identifying suitable water and soil assets for cultivating lots of plants, whilst concurrently striving to put into effect sustainable agricultural practices. This research gives an automated farm tracking and manage gadget that leverages Internet of Things (IoT) generation to deal with these challenges. The proposed device integrates computerized drip irrigation and dynamic sunlight shading to enrich crop productiveness, conserve water sources, and maintain healthful soil situations. By making use of temperature, humidity, and soil moisture sensors, the gadget continuously video displays units environmental situations and triggers automatic responses through an Arduino microcontroller (ESP32). The gathered records from the sensors are compared with predefined database values to optimize irrigation and shading mechanisms in real-time. Additionally, a mobile utility, advanced using Flutter, offers farmers with real-time farm facts, manage capabilities, and more suitable decision-making support. The device was tested through a prototype, demonstrating a 10.45% reduction in water intake and improved plant fitness. Compared to existing IoT-primarily based agricultural solutions, this system uniquely consists of a dynamic shading mechanism to protect crops from excessive heat, a feature not formerly addressed in similar studies. Conclusions indicate that this smart agricultural system can improve work effort, improve accurate agriculture while improving efficiency and stability, making it a promising solution for future sustainable agricultural development.

Rice leaf diseases pose a significant and escalating threat to global food security. Timely and accurate detection, particularly in the critical early stages characterized by subtle lesions, is paramount for effective disease management. However, existing solutions often struggle with the complexities of real-world field environments (e.g., variable lighting, occlusions, complex backgrounds), computational constraints on edge devices, and limited generalizability across diverse disease types and plant species. To address these challenges, this study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection. Our key innovations include: (1) A Multi-branch Large-kernel Fusion Depthwise (MLFD) module enhancing multi-scale contextual feature extraction critical for identifying subtle early lesions; (2) A Multi-scale Dilated Transformer Attention (MDTA) module integrating spatial and channel attention mechanisms to improve feature representation under complex conditions; (3) A Lightweight Detection Head (Lo-Head) optimized with grouped and depthwise convolutions, drastically reducing model complexity without sacrificing accuracy. Crucially, extensive experiments demonstrate the framework's superior performance. On a dedicated rice leaf disease dataset, it achieves a mean Average Precision mAP@0.5:0.95 of 62.62%, outperforming state-of-the-art lightweight detectors including YOLOv5n (56.73%), YOLOv8n (57.41%), YOLOv10n (56.14%), and the baseline YOLOv11n (60.85%), while maintaining low computational demands (6.3 GFLOPs, 2.66M parameters). Significantly, rigorous generalization experiments validate the model's exceptional transferability. Evaluated on independent datasets encompassing potato and tomato leaf diseases, the proposed framework consistently surpasses comparable models in mAP@0.5:0.95, demonstrating its robust capability to detect diseases across different plant species. This combination of high accuracy, computational efficiency, and remarkable cross-crop generalizability positions our framework as a highly promising tool for practical deployment on resource-limited edge devices (e.g., drones, field sensors) in smart agriculture systems, enabling proactive disease surveillance and precision control strategies across diverse crops.

This study presents a simulation-based approach to improving energy efficiency and security in IoT-based smart agriculture systems. The suggested architecture combines a new Blended Clustering Energy Efficient Routing (BCEER) protocol with an authentication mechanism based on Physical Unclonable Functions (PUFs) to respond to the main issues in resource-limited, sensor-oriented settings. BCEER maximizes cluster head selection using adaptive energy thresholds to minimize communication overhead and ensure extended network lifetime. Moreover, authentication based on PUF provides hardware-level identity authentication through challenge-response pairs, without demanding heavy cryptography computation, to improve security. For safe signal processing, polynomial approximation techniques were analyzed for sign function approximation, and error analysis indicated improving accuracy as the approximation degree grows. The findings show that the hybrid method enhances energy distribution, enhances authentication, and provides computationally efficient signal processing, making the system relevant for efficient and secure large-scale deployment in intelligent farming IoT networks.

Internet of Things (IoT) has revolutionized the world with its innovative and precise solutions catering a broad spectrum of applications. Smart cities, smart financial services, smart logistics, smart agriculture systems, smart healthcare management system are few applications worth mentioning. Device authentication and data security is a major concern in IoT networks as the devices used in these networks have inadequate computational processing and memory storage resources. Privacy is of paramount importance in applications such as healthcare and finance. As complex algorithms cannot be implemented on the resource-constrained IoT devices, research in the aforementioned areas is still an active subject. In this paper, we propose a mutual device and server authentication protocol using Physical Unclonable Functions (PUFs) for IoT devices. PUFs can be used to uniquely identify the devices and can also be used to generate random keys. As the name suggests, the keys generated by the PUFs cannot be reproduced. Using this unclonability feature, an authentication protocol has been developed. An IoT based temperature and humidity sensing module is developed to integrate the protocol and validate its effectiveness in providing the device authentication and data security.

With the requirement for new and effective procedures to improve crop cultivation, blending of modern technology, such as the Internet of Things (IoT), and ancient agricultural methods holds greater relevance. In the following study, the feasibility and importance of implementing an IoT-based smart crop cultivation and monitoring system are discussed. The smart agricultural technology will provide control, anti-toxic, and an optimum level of environment to yield crops in any remote or short on resources areas across the globe and beyond. The smart agricultural system enables professionals to manage, monitor, and analyze crop yields remotely with the help of an integrated mobile application. With limitations in natural resources and time, crop cultivation needs to be robust but safe and technologically empowered through smart agricultural systems such as the one we are presenting. The research work aims to mitigate the carbon footprint associated with conventional agriculture, add more precision to the crop yield by offering an optimized farming space, educate the farmers about advanced technology, and most importantly, enable quality crop cultivation in remote regions. With the help of IoT, the proposed procedure for crop cultivation gets precision in results, scalability for resources, remote control, and an optimized environment for cultivating multiple crops.

Currently, the primary focus of every country worldwide is to enhance the efficiency of daily human life by embracing smart technology. Achieving a smart country status involves leveraging the internet, which enables various forms of smart work, predominantly through automation. Automation can be accomplished using Wireless Communication, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (a subset of AI). Artificial Intelligence's role is constantly growing in human life and exerting a significant influence on society. In the context of Indian farming, Artificial Intelligence is progressively making its way and playing a vital role. In recent times, numerous countries have adopted machine learning and Artificial Intelligence in agriculture to predict and forecast crop yields, grade food, implement automated irrigation systems, and enhance the quality and quantity of crop production. This paper presents a comprehensive review of prior research conducted by various scholars, aiming to provide an overview of the applications and significance of Artificial Intelligence in agriculture, paving the way for automated and smart agricultural systems.

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

Over the years it has really been challenging working with Internet of Things (IoT) devices in terms of security as every IoT device offers a potential entry point. Blockchain is an emerging technology which is capable of minimizing the inherent risk of security and privacy issues of IoT. In this paper, blockchain IoT (BIoT)-based approach has been proposed to develop a smart agricultural system. It uses a distributed ledger which automatically stores the sensor data across different locations in a decentralized manner. Proposed method is beneficial compared to conventional IoT-based system as BIoT offers public access to the distributed ledger and time stamping of the sensor data. For the smart agriculture system, temperature and humidity of environment, moisture content and dielectric soil moisture content which are the fundamental parameters are measured here with three different sensors. NodeMCU has been used as IoT device. An Ethereum blockchain created via Ganache. Latency rate for the same is calculated.KeywordsSmart agricultureInternet of ThingsBlockchainEthereumSmart contracts

Abstract. Terrestrial net CH4 surface fluxes often represent the difference between much larger gross production and consumption fluxes and depend on multiple physical, biological, and chemical mechanisms that are poorly understood and represented in regional- and global-scale biogeochemical models. To characterize uncertainties, study feedbacks between CH4 fluxes and climate, and to guide future model development and experimentation, we developed and tested a new CH4 biogeochemistry model (CLM4Me) integrated in the land component (Community Land Model; CLM4) of the Community Earth System Model (CESM1). CLM4Me includes representations of CH4 production, oxidation, aerenchyma transport, ebullition, aqueous and gaseous diffusion, and fractional inundation. As with most global models, CLM4 lacks important features for predicting current and future CH4 fluxes, including: vertical representation of soil organic matter, accurate subgrid scale hydrology, realistic representation of inundated system vegetation, anaerobic decomposition, thermokarst dynamics, and aqueous chemistry. We compared the seasonality and magnitude of predicted CH4 emissions to observations from 18 sites and three global atmospheric inversions. Simulated net CH4 emissions using our baseline parameter set were 270, 160, 50, and 70 Tg CH4 yr−1 globally, in the tropics, in the temperate zone, and north of 45° N, respectively; these values are within the range of previous estimates. We then used the model to characterize the sensitivity of regional and global CH4 emission estimates to uncertainties in model parameterizations. Of the parameters we tested, the temperature sensitivity of CH4 production, oxidation parameters, and aerenchyma properties had the largest impacts on net CH4 emissions, up to a factor of 4 and 10 at the regional and gridcell scales, respectively. In spite of these uncertainties, we were able to demonstrate that emissions from dissolved CH4 in the transpiration stream are small (<1 Tg CH4 yr−1) and that uncertainty in CH4 emissions from anoxic microsite production is significant. In a 21st century scenario, we found that predicted declines in high-latitude inundation may limit increases in high-latitude CH4 emissions. Due to the high level of remaining uncertainty, we outline observations and experiments that would facilitate improvement of regional and global CH4 biogeochemical models.

“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

Using the idea of IOT this project explain the multi-task farming system using Arduino. The system uses a Wi-Fi module in which system is connected to the internet. A motor and two valve are controlled by this module for transporting the water to the farm on receiving the signal from a water level sensor and soil moisture indicator. This system explain the illustration of Internet of Things (IOT). This concept does the work like weeding, spraying water, harvesting, etc. The system also does the work like determining the humidity of the soil and measuring the physical environmental factor which can be monitored by any individual from anywhere they want and the data gets recorded in the database of the webpage or the app through IOT connection and the individual gets a notification on their cellular phone through message so that he can operate the system through a push message or can be done through the mobile app if there is any requirement to the field that to be taken care of.