# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 1 Introduction  
In May 2020, the European Commission announced two pesticide reduction targets as part of the Farm to Fork Strategy: a 50% reduction in the use and risk of chemical pesticides and a 50% reduction in the use of more hazardous pesticides. In Portugal, in the period from 2000-2020, despite the sale of plant protection products per used agricultural area having decreased by 38%, the total consumption of these products was 9706 tons, of which25% was herbicides used for weed control [1]. In Mediterranean regions, weed control in rainfed fodder crops under no-till farming is usually necessary before sowing crops and after crop emergence [2]. Sustainable agriculture is essential for future food security. The current reliance on chemical inputs like plant protection products (PPPs) and fertilizers contributes to pollution, biodiversity loss, climate change, and resource depletion, posing risks to the environment and human health. Balancing effective farming with reduced hazardous inputs requires adopting innovative technologies and efficient agricultural practices [3]. It is important to adopt new management approaches and instruments such as remote sensing and VRA technologies capable of reducing the amount of herbicide used with the environmental and operation cost-benefit analysis without affecting agricultural yield [4; 5]. Unlike satellite approaches that often require complex algorithms based on large datasets, proximal remote sensing approaches aim to provide users with cheap, simple, and open-source technologies to design their own decision support tools [6]. Many remote sensors have been used so far to monitor plants over time to manage crop protection [7]. Commercially available systems, e.g., WeedSeeker (Ntech Industries Inc., Ukiah, CA, USA), distinguish green plant material from the soil and other background elements and spray only where plant material is present in pre-emergence weed control. Post-emergence control often benefits from artificial intelligence methods, as they can effectively distinguish between the green plant material of different species and the background [8; 9]. Weeds do not appear homogeneously in the plot, so it may be appropriate to eliminate them according to their presence in each specific point of the plot. The risk assessment examines the potential damage caused by pests, including weeds, and determines the need for direct control using economic thresholds or development models. Although the focus is mainly on other enemies, weeds are also considered [10]. Leveraging precision agriculture technologies and data-driven insights helps monitor and control pest and weed infestations more efficiently, ultimately enhancing crop production [11]. Machine learning and deep learning methods can analyze high-dimensional data with unknown statistical characteristics for precision crop protection by learning the model structure directly from training data [12; 13; 14] and have proven to be very efficient in locating weeds in crops for site-specific weed management [15] based on shape descriptors with supervised or unsupervised learning methods. To know and quantify their presence at each of these points with a high level of certainty, the collection of close images can be a crucial technique due to its high level of resolution [16]. Adequate instruments and time are needed to filter and process data into information. The classification of the images must be evaluated and validated to assess the quality of detection of the different constituents of the image. An example of these validation methods is the kappa coefficient [17], whose objective is to provide a basis for comparing classified and reference data and verifying whether they agree. Table 1 shows [PERSON]'s kappa coefficient matrix. Here, the value of A corresponds to the sum of a, d, and g; the value of D corresponds to the sum of a, b and c; T corresponds to the sum of the rows which must be equal to the