The deployment of IoT sensors in agriculture changes farming practices, enabling real-time monitoring and data-driven decision making. Existing systems utilize various sensors transmitting data, often to the cloud, via established wireless networks such as Wi-Fi, cellular networks, or short-range communications like Zigbee. While effective for measuring environmental information such as like temperature or soil moisture, these conventional systems find it hard to meet the demands of high-resolution visual monitoring applications like crop fruiting detection. Key limitations arise from the difficulty in reliably transmitting large image files over the expansive areas common in agriculture, as standard wireless solutions often present trade-offs between range, bandwidth, and power consumption unsuitable for this data type. To address these critical gaps, this study introduces a long-range sensor network system specifically designed for efficient agricultural image acquisition and transmission. The presented approach brings two core strategies: first, it uses state of the art image compression done directly on the edge device, optimized to reduce images into compact latent vectors while preserving essential visual information. Secondly, it incorporates a low-power, long-range wireless communication technology chosen for its ability to ensure reliable data transfer across extensive agricultural terrains without excessive energy demands. Experimental evaluation using appropriate low-power computing hardware and relevant agricultural image data confirmed the system’s feasibility. The results demonstrated the effectiveness of on-device compression and validated the reliable transmission of the compressed image data over considerable distances.