Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings

Abstract

Agriculture directly affects the economic conditions of countries around the world; it is a sector that determines the prosperity and standard of living of people. The history of agriculture covers the process from the existence of man to the present. The crops obtained from agriculture are the source of food that humans and many other living beings need to sustain their life activities. Every year, crops derived from agricultural products suffer significant losses in their productivity and quantity due to weed infestation. Depending on the level of weed control, yield losses can range from 10% to 100%. In this study, an artificial intelligence based approach was proposed to identify weeds unintentionally growing on agricultural land and increase the control rate, considering technological developments. A dataset consisting of twelve species of seedlings and weeds was used for the analysis of the study. The proposed approach trained the dataset using pre-trained deep learning models and was optimized by metaheuristic optimization methods by selecting the species-based activations that were transferred to Softmax, which is in the final layer of the deep learning models. Then, the species-based activations that were improved by the optimization methods were reclassified by Softmax. The result of the experimental analysis was an overall accuracy of 99.39%. This overall accuracy was achieved by jointly using the DarkNet model and the Gaining-sharing knowledge-based optimization method together.