A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0

In the convolutional neural network, the precise segmentation of small-scale objects and object boundaries in remote sensing images is a great challenge. As the model gets deeper, low-level features with geometric information and high-level features with semantic information cannot be obtained simultaneously. To alleviate this problem, a successive pooling attention network (SPANet) was proposed. The SPANet mainly consists of ResNet50 as the backbone, successive pooling attention module (SPAM), and feature fusion module (FFM). Specifically, the SPANet uses two parallel branches to extract high-level features by ResNet50 and low-level features by the first 11 layers of ResNet50. Then, both the high- and low-level features are fed to the SPAM, which is mainly composed of a successive pooling operator and a self-attention submodule, for further extracting deeper multiscale and salient features. In addition, the low- and high-level features after the SPAM are fused by the FFM to achieve the complementarity of spatial and geometric information. This fusion module alleviates the problem of the accurate segmentation of object edges. Finally, the high-level features and enhanced low-level features of the two branches are fused to obtain the final prediction results. Experiments show that the proposed SPANet achieves a good segmentation effect compared with other models on two remotely sensed datasets.

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SPANet: Successive Pooling Attention Network for Semantic Segmentation of Remote Sensing Images

In this article, we present a supervised machine learning multipath and administrative-distance-based load balancing algorithm for an Agriculture Internet of Things (AG-IoT) network. The proposed algorithm is known as an artificial intelligence or simply AI-enabled multihop and administrative-distance-based opportunistic routing (MHADBOR) algorithm, which processes the collected information from source to the destination by means of multihop count and administrative-distance-based communication infrastructure in the network. Beside that, we used cluster heads (CH), microbase stations ($\boldsymbol {\Re BS}$ℜBS), and macrobase stations ($\boldsymbol {\aleph BS}$ℵBS) in the network with a frequent rate to effectively utilize the administrative distance while managing the deployed network traffic in a congestionless communication environment. In addition, the MHADBOR algorithm empowers the participating devices to practice the administrative distance rather than hop count communication when they are in the vicinity of network special components, e.g., CH and $\boldsymbol {\Re BS}$ℜBS outcome statistics of the MHADBOR algorithm in the simulation environment exhibit an extraordinary improvement in contention, congestion, communication, and computing costs, accompanied by throughput and end-to-end (E2E) delay and packet loss ratio in the deployed AG-IoT network. 

MHADBOR: AI-Enabled Administrative-Distance-Based Opportunistic Load Balancing Scheme for an Agriculture Internet of Things Network

Technological revolutions in smart farming: Current trends, challenges & future directions

Security in IoT-enabled smart agriculture: architecture, security solutions and challenges

Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques.

AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture

The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate.

Tanmay Anand

Papers

AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture

A blockchain and deep neural networks-based secure framework for enhanced crop protection