In a diffusion-based molecular communication system, molecules are employed to transmit information. When multiple absorbing receivers exist in an environment, the analysis results of channel characteristics for one absorbing receiver cannot be generalized because of the mutual interferences caused by multiple absorbing receivers. This letter investigates how the unintended receiver affects the intended receiver under the scenario of a point transmitter and two absorbing spherical receivers. Using the empirical inferences and simulation fitting algorithm of Levenberg-Marquardt, we propose the channel model by presenting cumulative absorbing probability and absorption probability density expressions on the intended receiver with respect to the variables of angle, distance, and time. The proposed channel model is validated through numerous particle-based simulations. The analysis results reveal that the peak time of the molecular concentration will not be influenced by the unintended receiver.

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture.

Recently, anomaly detection or localization in molecular communication (MC) system has become a research hot topic that can be used to distinguish the abnormal cells in the human body. Due to no or very few molecules the target released, it is difficult to detect the silent target in the MC system. In this article, we construct a three-node MC (TNMC) system, including a point source transmitter, a passive receiver, and a silent absorbing target (SAT), the SAT cannot release but has the ability to absorb the molecules. Based on the above system, we propose a probability equivalent modeling (PEM) method to model the channel of the TNMC system and derive the channel impulse response (CIR) of the receiver. Besides, we propose a localization algorithm based on maximum-likelihood estimation (MLE) and Newton–Raphson method to spot the relative location of the SAT. Simulation results verify the correctness of the PEM method and effectiveness for localizing the target.