Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops

Abstract

The increasing use of plastics in rural environments has led to concerns about agricultural plastic waste (APW). However, the plasticulture information gap hinders waste management planning and may lead to plastic residue leakage into the environment with consequent microplastic formation. The location and estimated quantity of the APW are crucial for territorial planning and public policies regarding land use and waste management. Agri-plastic remote detection has attracted increased attention but requires a consensus approach, particularly for mapping plastic-mulched farmlands (PMFs) scattered across vast areas. This article tests whether a streamlined time-series approach minimizes PMF confusion with the background using less processing. Based on the literature, we performed a vast assessment of machine learning techniques and investigated the importance of features in mapping tomato PMF. We evaluated pixel-based and object-based classifications in harmonized Sentinel-2 level-2A images, added plastic indices, and compared six classifiers. The best result showed an overall accuracy of 99.7% through pixel-based using the multilayer perceptron (MLP) classifier. The 3-time series with a 30-day composite exhibited increased accuracy, a decrease in background confusion, and was a viable alternative for overcoming the impact of cloud cover on images at certain times of the year in our study area, which leads to a potentially reliable methodology for APW mapping for future studies. To our knowledge, the presented PMF map is the first for Latin America. This represents a first step toward promoting the circularity of all agricultural plastic in the region, minimizing the impacts of degradation on the environment.

Keywords: Agricultural plastic waste (APW); Artificial intelligence (AI); Earth observation (EO); Mulching; Reverse logistics planning; Sentinel.