Two methods are commonly used for pesticide spraying: manual knapsack spraying in which human labor carries the equipment and performs spray on every plant and broadcast spraying via a tractor-mounted sprayer in which the entire field is sprayed indiscriminately. Both methods are imprecise and, therefore, cause serious damage to farmers' health (due to first-hand/direct exposure) and to the environment (due to overdosing) [2; 3; 4; 5; 6]. Despite its hazards, agrochemical spraying is still common in practice as it is a viable and economical means to protect tobacco crop from pests and pathogens [7]. The solution, therefore, lies not in eliminating the use of agrochemicals but instead in optimizing their application by embracing advanced techniques and methodologies. Artificial Intelligence is rapidly bringing a substantial paradigm shift in the agriculture sector. Endowing agricultural spraying systems the cognitive ability of understanding, learning, and responding to different crop conditions greatly improves spraying operations. Precision spraying methods combine techniques from emerging disciplines such as artificial intelligence, robotics, and computer vision, which provides a spraying system the ability to identify plants (crop) and weeds and apply precise doses only on the desired targets [8; 9; 10; 11; 12; 13]. Over the last decade, numerous promising attempts have been made by researchers for the development of intelligent spraying systems for different crops [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Surprisingly, not much work is found in the literature on vision-based site-specific spraying systems for crops. The vision-based system tends to deal with numerous variations, such as varying leaf sizes at different growth stages, varying light intensities, different soil textures, varying leaf colors due to different water levels, high weed densities, and crop plant occlusion by weeds, etc. Existing methods for vision-based plant/weed detection and precision spraying are mostly based on traditional machine learning-based techniques [24; 25; 26; 27; 28; 29; 30; 31; 32]. Although high accuracies have been achieved with these techniques, the hand-crafted features formulation and generation of a decision function over the extracted features make them less robust. Therefore, they are certainly not a preferred choice for tobacco plant and weed detection (keeping in view the factors of variations and complexities involved in tobacco fields) due to poor generalization capabilities. Over the past few years, deep learning-based computer vision algorithms have demonstrated their ability to perform well on complex problems from training examples [33; 34; 35; 36; 37; 38; 39; 40; 41]. CNNs are the main architecture of these computer vision algorithms. Deep learning algorithms learn the features and decision functions in an end-to-end fashion. [PERSON] et al. [42] proposed a classifier known as gaNet-C for type-of-traffic forecast problem. An additive network model, gaNet, has the capability to forecast k-steps beforehand by utilizing time-series of last computed values for each node. The proposed model demonstrates good performance on two detection forecast problems. The advantages that deep learning algorithms offer, such as feature learning capabilities, high accuracy, and better performance in intricate problems, make them best suited for complex tasks such as detecting tobacco plants under several variations in outdoor fields. Several studies have been reported with respect to deep learning-based plant and weed detection [43; 44; 45; 46; 47; 48; 49]. The latest research on plant and weed detection mainly utilizes computer vision [50; 51; 52; 53; 54; 55; 56]. For instance, [PERSON] et al. [57] used deep learning for finding defects in  
Figure 1: Weeds and tobacco leaf infestation due to pests. tomatoes by applying Deep ResNet classifiers. According to their finding, ResNet50 with fine-tuned layers was reported as the best model that achieved an average precision of 94.6% and a recall of 86.6%.