# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 1 Introduction  
Never before has the use of pesticides, and especially herbicides, been at the centre of a global debate that has involved multifaceted issues making this matter increasingly complex [1; 2]. Although the evidence of the negative effects on health and the environment caused by the use of herbicides has been underlined by many authors [3; 4], we have recorded a significant increase in applied quantities over the last decades [5; 6], confirming, for the time being, the reliance of agriculture on their use. Despite the increasing availability of herbicides that can be used at low doses and the efforts made by the EU to regulate this matter [7], in 2020, almost 346,000 tonnes of herbicides were sold in Europe [8]. Many recent research activities have been devoted to reducing herbicide consumption in order to support the transition from conventional to agroecological cropping systems [9; 10]. According to [11], the first step in this process is to reduce the external-farm input use by making their use more efficient. For the last six decades, integrated pest management (IPM) has been the main response provided by researchers to improve crop protection strategies, and although its adoption has not proved to be as effective as hoped [12], the evaluation of weed infestation levels and the establishment of the action threshold is one of the pillars of integrated weed management (IWM) [13; 14]. Nevertheless, the weed control threshold has been questioned as a problem-solving concept. The reasons are numerous [15; 16]: (i) the costs and difficulties of determining the real incidence of weed presence in relation to their random distribution in fields; (ii) the unreliability of models used to estimate yield losses because of their inability to consider thedifferent competition level attributable to each weed species and the effects of other factors influencing crop production (e.g., weather conditions, soil nature, farming practices); and (iii) the need to consider the long-term effects caused by the weed capability to produce a large number of seeds which make weed control very difficult in the next years. To overcome these problems, many different types of weed control thresholds have been proposed. The economic threshold is the most known and is defined as the weed development that can cause a crop yield loss whose value equals the cost of weeding, but other types of thresholds have been developed in relation to damage, period, action and long-term effects [17; 18]. Regardless of the type, the functionality of the threshold concept depends on two features: (i) the parameter chosen as a proxy to evaluate the competition level exerted by the weed population, and (ii) how closely this parameter is correlated to the consequent crop yield loss. In the past decades, empirical models have been developed to predict yield losses starting from weed density values measured in the early post-emergence stages [19; 20], but their efficacy was limited since neither the emergence time nor the different competitive capacities of weeds were considered [21]. Various authors [22; 23] proposed new methods based on the measure of leaf area, rather than density, to evaluate effective weed development. Although these models were found to have a better predictive capability [24], their practical use has been hindered by the lack of fast and viable methods for leaf area data collection [25]. Recently, more sophisticated algorithms have been proposed to estimate crop yield loss by considering the duration of weed competition and the stochastic effect of weather conditions [1], but all of these attempts to support weed management decisions showed serious limitations in field applications [26]. Nowadays, the development of unmanned aerial vehicle (UAV) technology can represent new powerful opportunities to overcome the abovementioned limits and to analyse the whole field area [9; 10]. Many UAV applications have been used to capture field images that, when properly processed, can provide precious information about crop well-being.