###### 1 Introduction  
Over the past decades, the agricultural industry has benefited from the use of pesticides to minimize crop losses [1]. Globally, 20 to 40% of crop production is estimated to be lost due to pests, leading to an economical cost of USD 220 billion according to the Food and Agriculture Organization (FAO) from the United Nations [2], and hence the current use of approximately 4 million tons of pesticides to protect the nearly 3 billion metric tons of crops produced each year [3, 4]. A predominant amount of pesticide is usually conventionally applied, resulting in uncontrolled and non-targeted release with only a small fraction reaching the desired target organism [5]. This challenge has led to agricultural practiceswith indiscriminate overuse of pesticides, exacerbating the risks to the environment and human health [6; 7]. In particular, prolonged pesticide application increases the risks of environmental toxicity due to field runoff, leaching, volatilization, and spray drift, among other factors [8; 9], in addition to the emerging concerns of the persistence of pesticide residue found in food and livestock [10; 11]. Due to increased pesticide residue concerns, there have been collective efforts by international and national entities (e.g., EPA, USDA, FAO, Codex Alimentarius Commission, World Health Organization, etc.) to establish policies and regulations regarding the maximum residue limits [12; 13; 14; 15]. In order to comply with the pesticide's regulations, several analytical methods have been developed and validated to accurately quantify the concentration of pesticide residue. Some of the widely used analytical methods involve techniques such as gas chromatography, high performance liquid chromatography, liquid chromatography with mass spectrometry, supercritical fluid chromatography, and capillary electrophoresis, among others [16; 17; 18]. Although these analytical techniques possess high accuracy and specificity, they also require expensive instrumentation and supplies, controlled laboratory environments, and time-consuming sample preparation. This therefore imposes a major economical constraint to growers, hindering the USDA (United States Department of Agriculture)'s efforts towards socio-economical equity in the agricultural sector [19]. To address these challenges, there has been extensive work regarding the Agri-Tech revolution, with the development of precise farming [3]. This type of farming incorporates recent technological advancements in the Internet of Things (IoTs), Machine Learning (ML), and Artificial Intelligence (AI), among others, in the current agricultural practices [20; 21; 22]. These technological tools have enabled the digital automatization of processes related to data collection, decision-making, data processing, and data mining [21]. Recently, developed technological tools have been extended to disease detection, pest identification, crop management planning, and yield predictions [23; 24; 25; 26]. Furthermore, there has been some attraction towards the development of imaging pesticide residue analysis powered by AI to render more accessible, cost-effective, and non-destructive methods [27; 28; 29]. Nevertheless, the use of AI-powered tools and conventional imaging systems for pesticide residue analysis holds some considerable limitations [30; 31]. These are mainly related to pesticide residue transparency to digital cameras and high background signals from nonuniform leaf surfaces, leading to low accuracy in the analysis [32; 33]. These are in addition to the shortage of labeled and annotated data from both controlled and non-controlled environments, which play a major factor in the design of the system and the developed algorithms [34; 35]. Recent studies have overcome some of these limitations by focusing their imaging analysis on the inherent IR or fluorescent properties of some pesticides [36; 37; 38].