# A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images
## 1 Introduction  
Precision agriculture and measures have improved production, quality, and working conditions by reducing manual labor. These things help make farming sustainable. Modern farmers use digitally controlled farm plants and unmanned aerial vehicles (UAVs) for monitoring and forecasting. Affordable drones can image ground data with geolocations. This helps users understand ground information better. Multispectral and RGB drones can image the near-infrared section of the electromagnetic spectrum above crops, revealing their health [1]. Satellites, aerial photography, unmanned aerial vehicles (UAVs), geographic information systems (GIS), global positioning systems (GPS), variable rate application (VRA), and geostatistics are all examples of remote sensing utilized in precision agriculture. This method of geostatistics is a branch of applied statistics that is closely linked to precision farming. Through data transformation, it is possible to discover the values of the attributes at unsampled points on the field by calculating their spatial dependency and spatialstructure. Interpolation is a common technique for estimating values at nearby points when exact values are unavailable. Variography is a type of geographical modeling utilized in precision agriculture, while kriging is the term used to describe spatial interpolation [2]. Precision farming has numerous significant applications in arable land, one of which is enhancing the efficiency of fertilizer utilization based on the three macronutrients, namely nitrogen, phosphorus, and potassium. In conventional agricultural practices, fertilizers are typically applied uniformly across the entire field at predetermined intervals throughout the year. Precision farming enables the optimization of agricultural practices by increasing applications in certain locations and decreasing applications in others. Numerous environmental hazards are commonly attributed to the escalating usage of agricultural inputs, resulting in the leaching of nitrogen and phosphorus from fields into both surface and groundwater sources. The utilization of PF technology enables the precise application of fertilizers in varying spatial and temporal dimensions, therefore enhancing the efficacy of variable rate application [1; 3]. Weeds are a major threat to crops because of their detrimental effects on neighboring plants and their ability to stunt plant growth and reduce nutrient intake. If weeds are not controlled quickly and efficiently, they can diminish agricultural output by 20-80% [4]. Weeds compete with crops for limited resources, making them a problem in agricultural settings. Their strong competition with agricultural plants for nutrients, water, sunlight, air, etc. affects production and yield, which have monetary value. Weeds reduce output by 34% worldwide [4]. Wheat yields drop 48-60% [5] and sesame yields drop 50-75%. Weeds degrade crop output and quality. Farmers spend billions of dollars yearly on weed management in an attempt to achieve satisfactory weed control and higher crop productivity. Reduced yields and worse quality products are the direct results of ineffective weed management in horticulture crops [6]. The use of chemical and cultural control strategies may have unintended consequences for the environment. Low-cost technology for early weed identification and mapping will make weed management more effective and sustainable. Using early weed management can cut down on agricultural diseases and pests and boost crop yields by as much as 34% [7]. Many strategies exist for weed control, and each one is eco-friendly in some way. One of the most promising of these techniques is image processing. Image processing makes use of UAVs (unmanned aerial vehicles) to survey fields for weeds and other unwanted plants. UAVs are useful in agriculture because of their speed and efficiency in covering large areas, without damaging the soil or compacting it [8]. Converting UAV data into actionable intelligence remains a significant hurdle.