Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems.

The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants.

The temperature and humidity control of a pig house is a complex multivariable control problem. How to keep the temperature and humidity in a pig house within a normal range is the problem to be solved in this paper. The traditional threshold-based environmental control system cannot meet this requirement. In this paper, an intelligent control system of temperature and humidity in a pig house based on machine learning and a fuzzy control algorithm is proposed. We use sensors to collect the temperature and humidity in the pig house and store these data in chronological order. Then, we use these time series data to train the GRU model and then use the GRU model to predict the temperature and humidity change curve in the pig house in the next 24 hours. Finally, the mathematical model of the pig house and related equipment is established, and the output power of the related equipment is calculated based on the prediction results of GRU so as to effectively regulate the indoor temperature and humidity. The experimental results show that compared with the threshold-based environmental control system, our system reduces the abnormal temperature and humidity by about 90%.

To comprehensively capture conversational emotion information within and between modalities, address the challenge of global and local feature modelling in conversation, and enhance the accuracy of multimodal conversation emotion recognition, we present a model called Multimodal Transformer and GNN for Emotion Recognition in Conversations (MTG-ERC). The model incorporates a multi-level Transformer fusion module that employs multi-head self-attention and cross-modal attention mechanisms to effectively capture interaction patterns within and between modalities. To address the shortcomings of attention-mechanism-based models in capturing short-term dependencies, we introduce a directed multi-relational graph fusion module, which employs directed graphs and multiple relation types to achieve efficient multimodal information fusion and to model short-term, speaker-dependent emotional shifts. By integrating the outputs of these two modules, the MTG-ERC model effectively combines global and local conversational emotion features and enhances intra-modal and inter-modal emotional interactions. The proposed model shows consistent improvements (around 1% absolute) in both accuracy and weighted F1 on the IEMOCAP and MELD datasets when compared with other baseline models. This highlights the model’s strong performance indicators and validates its effectiveness in comparison to existing models.

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence.