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The world population is growing very fast and with this increase, the need for food has been increased briskly. With the advent of the Internet of Things (IoT) technology, this era is witnessing a shift from traditional farming methods to advanced approaches. IoT is an emerging paradigm that connects different smart objects physically by using the best smart farming practices for the modernization of the livestock industry. Several IoT-based solutions have been introduced to automatically monitor, track, and manage livestock farming with minimal human intervention. This systematic literature review (SLR) presents a comprehensive discussion on major IoT-based livestock applications, state-of-the-art sensor/devices, communication protocols, and new multidisciplinary technologies. The SLR has been compiled by reviewing the research studies published between 2016 and 2022 in well-reputed databases. A total of 879 papers were identified systematically out of which 30 were selected and classified accordingly. Furthermore, a rigorous discussion on relevant technologies such as machine learning, big data, cloud computing, and artificial intelligence has been presented by developing network architecture, topologies, and platform. Besides, we have presented open issues as well as security challenges and discussed a use case for an IoT-livestock health monitoring system (IoT-LHMS) for key management and end-to-end secure communication among nodes and gateway. In the end, we proposed an IoT-enabled livestock management taxonomy based on major components and presented future research directions

Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings’ kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model’s performance and the classification effect are excellent.