Coconut production has a significant impact on the livelihoods of many farmers worldwide, serving as a primary source of income. However, recent observations have revealed a concerning decline in tree health and leaf condition, attributed to prevalent diseases and soil nutrition deficiencies. This deterioration poses a direct threat to productivity, gradually weakening coconut trees’ strength, nutrient absorption capacity, and yield production. Recognizing the urgency of this issue, this paper proposes an innovative approach leveraging Deep Learning, specifically Convolutional Neural Networks (CNNs), and the interconnected network of physical devices commonly referred to as the Internet of Things (IoT). The primary objective of this project is to develop a comprehensive web application for early coconut disease detection using CNN. Advanced technologies, including classification and image processing are integrated to assess tree health, identify disease symptoms, and detect pest infestations. Beyond disease detection, the project incorporates a recommendation system for pesticides to effectively cure identified diseases. Additionally, Internet of Things (IoT) technology is employed to analyze soil conditions, providing insights that inform personalized fertilizer recommendations. This proactive system not only mitigates current challenges faced by coconut farmers but also establishes a sustainable framework for optimizing productivity. The overall performance of this architecture is validated with an impressive accuracy measure of 97.2%.