Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. However, local model or weight updates can still leak sensitive information. Secure aggregation protocols mitigate this risk by ensuring that only the aggregated updates are revealed. Among these, single-setup secure aggregation protocols, where key generation and exchange occur only once, are the most efficient due to reduced communication and computation overhead. However, existing single-setup secure aggregation protocols often lack support for dynamic user participation and do not provide strong privacy guarantees such as forward and backward secrecy.

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks.

Abstract Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi‐robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta‐heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi‐robotics to travel from the initial point to such goal. The fundamental purpose of portable multi‐robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio‐inspired methods that are exploited to optimize hybrid neuro‐fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real‐time scenarios. Several optimization approaches of bio‐inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi‐robot navigation accuracy in terms of navigation duration and length of the path.

Paddy residue burning is common across Indo-Gagantic plane i.e. Punjab and Haryana states of India. Rice-Wheat cropping system is intensively followed across the Punjab and Haryana. Every year, Rice is cultivated from May to Oct. followed by the Wheat from Nov. to April. Most of the farmers burn the leftover plant debris after Rice harvesting and clear the fields for the next cropping season. The burning of crop residues releases several particles and gases into the atmosphere which causes huge air-pollution. There is an urgent need to monitor such man-made burning to avoid/minimize the air-water pollution. Satellites such as MODIS and VIIRS provide active fire events data daily. We have attempted to analyze the data provided by VIIRS over Punjab and Haryana. This paper attempts to answer a few research questions such as 1. How are the state and zone-wise trend in active fire events cropping seasons of 2017, 2018, and 2019, 2. When is the peak burning period across Punjab and Haryana during 2017, 2018 and 2019 and 3. What is the district-wise percent change in burning across Punjab and Haryana. Our analysis shows that overall burning reduces by 22% in Punjab compared to both 2017 and 2018 and 49% and 61 % in Haryana. Malwa region of Punjab and Hissar division of Haryana was more prone to burning. We have observed that 26 Oct. to 8 Nov. is a peak time of burning in Punjab, however, maximum burning observed during 26 Oct. to 1 Nov. in Haryana. Maximum number of fire incidences were reported from Fate-habad, Sirsa, Jind and Kaithal, Karnal in Haryana. In Punjab, reports poured in from Sangrur, Bathinda, Ferozpur, and Patiala. This trend analysis of time and location can help administrators to optimize the on-ground human and machine resources.

Precision farming technologies are essential for a steady supply of healthy food for the increasing population around the globe. Pests and diseases remain a major threat and a large fraction of crops are lost each year due to them. Automated detection of crop health from images helps in taking timely actions to increase yield while helping reduce input cost. With an aim to detect crop diseases and pests with high confidence, we use convolutional neural networks (CNN) and boosting techniques on Corn leaf images in different health states. The queen of cereals, Corn, is a versatile crop that has adapted to various climatic conditions. It is one of the major food crops in India along with wheat and rice. Considering that different diseases might have different treatments, incorrect detection can lead to incorrect remedial measures. Although CNN based models have been used for classification tasks, we aim to classify similar looking disease manifestations with a higher accuracy compared to the one obtained by existing deep learning methods. We have evaluated ensembles of CNN based image features, with a classifier and boosting in order to achieve plant disease classification. Using an ensemble of Adaptive Boosting cascaded with a decision tree based classifier trained on features from CNN, we have achieved an accuracy of 98% in classifying the Corn leaf images into four different categories viz. Healthy, Common Rust, Late Blight and Leaf Spot. This is about 8% improvement in classification performance when compared to CNN only.