Abstract
Recently, many researchers and practitioners used Machine Learning (ML) algorithms in digital agriculture to help farmers in decision making. This study aims to identify, assess and synthesize research papers that applied ML algorithms in weed detection using the Systematic Literature Review (SLR) Protocol. Based on our defined search string, we retrieved a total of 439 research papers from three electronic databases, of which 20 papers were selected based on the selection criteria and thus, were synthesized and analyzed in detail. The most applied ML algorithm is Neural Networks in these models. Thirteen evaluation parameters were identified, of which accuracy is the most used parameter. 75% of the selected papers used cross-validation as the evaluation approaches, while the rest used holdout. The challenges most encountered were insufficient data and manual labeling of the pixel during image segmentation. Based on the ML algorithms identified, we concluded that supervised learning techniques are the most used techniques in weed detection.
Highlights
According to the United Nations, the global population will be around 8.5 billion, 9.7 billion, and 10.9 billion by 2030, 2050, and 2100, respectively [1]
439 papers were retrieved from the databases and 20 papers were selected for the analysis based on the study selection criteria
Proceedings, books, and reports are the categories in this study. 70% of the research papers were published in journal articles
Summary
According to the United Nations, the global population will be around 8.5 billion, 9.7 billion, and 10.9 billion by 2030, 2050, and 2100, respectively [1]. The global population and the agriculture product demands need to be at equilibrium to avert this crisis. SLR assists researchers and practitioners in identifying research gaps and trending issues Secondary studies, such as surveys and reviews that were excluded from the study due to the selection criteria outlined, are discussed here. [29] Conducted a comprehensive survey on deep learning techniques to detect and classify weed. The study reveals that the most challenging task in weed detection is the segregation of plant crops and weeds. Namely: livestock, crops, soil, and water management.[30] Surveyed the extraction of hidden patterns from agriculture datasets using deep learning techniques. [31] Reviewed automatic weed and crop discriminations using machine learning and sensing method. Addressing this research gap is the main purpose of this study
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