Cultivating Precision: A Comprehensive Approach to Automated Weed Detection through Advanced Deep Learning and Image Processing Techniques

Abstract

Humanity's journey into agriculture marked one of our earliest triumphs, laying the foundation for a thriving global agricultural sector. As our population burgeons, there arises an urgent need for a more resilient and productive agricultural system. Traditional practices, such as utilizing cow dung as fertilizer, once served to boost output, catering to the expanding demands of a growing populace. However, the pursuit of increased profits in later years spurred the advent of the "Green Revolution." Unfortunately, this revolution brought with it a surge in the use of potent chemical herbicides. While undeniably enhancing output, this approach has exacted a toll on the environment, raising concerns about the sustainability of our coexistence with this magnificent planet. In response to these challenges, measures have been instituted to curtail the use of herbicides only to areas where weeds genuinely pose a threat. In the present study, we harness the capabilities of MATLAB's built-in image processing tools to identify weed patches within field photographs. As the global population expands and the availability of arable land and natural resources diminishes, the concept of precision agriculture has captured the attention of scientists worldwide. This study posits that the challenges posed by the growing population and limited resources can be effectively addressed through the application of image processing techniques. By utilizing advanced technologies, we aim to enhance the efficiency and sustainability of agricultural practices, paving the way for a harmonious coexistence with our planet.