# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 1 Introduction  
'Water' is one of the most important resources required for crop production. In different stages of crops life cycles, they require different amounts of water. Water influences, among other things, respiration, photosynthesis, mineral nutrient translocation, absorption, mineral nutrient utilization, and cell division. Water scarcity has a huge impact on crop quality and yield. As a result, water has an impact on nutrient availability, operation timing, and other factors, in addition to having a direct impact on crop production [1]. As a result, crops require watering in order to grow and develop. Crop watering, also known as 'irrigation,' is a method used to help crops grow as an alternative to rain-fed farming. Canals, sprinklers, pipes, sprays, drips, pumps, and other man-made devices provide irrigation [2, 3]. According to the report of AQUASTA [4], water withdrawal ratios of the Earth's freshwater are 70% in the agricultural sector for crop irrigation, 11% in municipal, and 19% in industrial, indicating that agriculture is by far the largest consumer of the Earth'savailable freshwater. Meanwhile, freshwater accounts for only 0.5% of the world's water, with seawater accounting for the majority (97%) and frozen water accounting for the remaining 2.5% [5]. Irrigation needs are expected to increase agriculture's global water demand by 15% by 2050 [6]. Currently, artificially irrigated areas produce approximately 40% of the world's food [7]. Agriculture's water needs, on the other hand, already compete with people's and the environment's daily needs, particularly in areas where irrigation is required, threatening ecosystem survival. According to an OECD report, agriculture production is heavily reliant on water, and water threats are becoming more prevalent as agricultural regions around the world have faced water issues in recent years [7]. Furthermore, agriculture is both the primary user of water for agricultural production and the primary polluter of water due to the use of chemical pesticides and fertilizers. Moreover, in the coming years, climate change will have a significant and uncertain impact on water supply [7]. As a result, agricultural water management must be improved in order to make agriculture more sustainable, contributing to global food and water security. Irrigation scheduling is the process by which irrigators determine and manage crop watering frequency and duration. Farmers benefit from irrigation scheduling by increasing crop yield and quality while reducing water loss due to deep precipitation and runoff, lowering pumping costs, increasing water efficiency, and ensuring long-term sustainable water usage. Four parameters are required to successfully schedule irrigation: soil moisture content, soil water holding capacity, soil texture, and crop water use at various growing stages [8]. It is also necessary to consider the irrigation system's capacity. During the growing season, different crop types consume varying amounts of water. For example, canola consumes water at a rate of seven mm/day during pod fill, but consumes water at only two mm/day during the rosette stage. Peas, for example, can consume water at a maximum of six mm/day and no more than two mm/day during pod development [9; 10]. In this paper, we focus on soil-based methods because we are predicting water requirements before drought stress occurs. Based on soil moisture measurements, the soil-based approach calculates the amount of water currently available to the crop. Smart irrigation technologies are now being used to assist irrigators with on-site field moisture measurement in order to predict soil moisture values for optimal water usage [10; 11]. This prediction will be used to estimate and schedule irrigation in order to improve irrigation controls by tracking moisture-related conditions on the field and performing watering at optimal levels automatically [12]. The smart irrigation technology that this paper focuses on soil moisture-based smart irrigation.