Advancements in Smart Agriculture: A Comprehensive Review of Machine Learning and IOT Approaches

In contemporary agriculture, optimizing crop yield necessitates efficient weed management and precise irrigation. This review amalgamates insights from ten studies, centering on the integration of machine learning, deep learning, and IoT technologies to address these challenges. The weed detection segment underscores advanced solutions like attention-based deep networks and visual transformers, tackling crop-weed discrimination challenges, albeit contending with issues such as high computation costs. Transitioning to precision agriculture, the paper explores machine and deep learning techniques for superior weed identification and control, offering viable integration with herbicide applicators. Challenges encompass interoperability and standardization of sensing and control protocols. The narrative then shifts to smart irrigation systems, presenting IoT-based solutions for real-time monitoring and automatic irrigation. Systems like Arduino Mega 2560 and dynamic irrigation scheduling boost crop productivity, with advantages including farmer-friendly interfaces and limitations in potential implementation costs on larger scales. In the results analysis, a collaborative impact is evident, envisioning a seamless integration of advanced technologies for sustainable and productive agriculture. Collectively, these studies signal a promising trajectory in leveraging technology to address challenges, paving the way for a new era of precision farming amid the evolving agricultural landscape.