# Prediction of Leaf Break Resistance of Green and Dry Alfafla Leaves by Machine Learning Methods
## 1 Introduction
The literature shows that studies related to the evaluation of the mechanical qualities required to maintain the leaf quality of alfalfa or other forage crops, as well as the design and modernization of the equipment required for harvesting and threshing and the optimization of the operating parameters, are usually carried out under laboratory conditions and harsh field conditions. These techniques are exceedingly expensive, labor-intensive, and require a very drawn-out approach. Unconventional approaches can be employed in place of experimental procedures to precisely establish these required attributes in the current era where economy, energy, labor, and time are highly significant. The most popular non-conventional technique for figuring out the physical and mechanical characteristics of plant products is machine learning. Machine learning, which is a sub-branch of artificial intelligence, can be defined as a method that makes predictions by using inferences from past experiences and data [14]. It focuses on teaching computers to learn from data and improve them through experience rather than being explicitly programmed to do so. In machine learning, algorithms are trained to find patterns and correlations in large data sets and make the best decisions and predictions based on this analysis [15]. Machine learning algorithms are one of the extremely popular methods applied to classification and regression problems in many fields, such as medicine [16; 17], engineering [18; 19], economy [20], education [21; 22], business [23; 24], natural sciences [25; 26], sport sciences [27] and agriculture [28; 29]. [PERSON] et al. (2014) [30] utilized an artificial neural network (ANN) to predict some mechanical properties of melon fruit. [PERSON] et al. (2023) [14] determined some engineering parameters of cherry tomatoes using machine learning algorithms. [PERSON] and [PERSON] (2022) [31] estimated the rupture energy values of Deveci and Abate Fetel pear fruit using an artificial neural network (ANN). An artificial neural network can be used to better estimate the volume and surface area of a fruit according to [PERSON] et al. (2017) [32]. [PERSON] et al. (2023) [33] conducted an experiment on the determination of hazelnut's (_Corylus avellana_ L.) terminal velocity and drag coefficient based on some fruit physical properties using machine learning algorithms. By using computerized mathematical and statistical processes on data, this technique models systems that make predictions. It belongs to the science of artificial intelligence. It includes several algorithms and method architectures. Numerous technical developments, such as speech and pattern recognition, data analysis, and prediction, have been made possible through machine learning. Using training data, machine learning, which learns and develops autonomously based on experience without outside assistance, may categorize and predict [34; 35]. Knowing the leaf breaking resistance allows harvesting to be performed with a minimum leaf loss rate, thus minimizing leaf losses during harvesting and post-harvesting. This study aimed to provide an accurate prediction of the leaf break stress of alfalfa plants depending on some vegetative and mechanical parameters using machine learning. By using machine learning models, it was aimed to determine the most accurate model considering different inputs and network structures. The results obtained can be considered as an effective tool to deal with post-harvest losses of alfalfa leaves and to collect the necessary data for the optimization of existing processing systems and the design of the necessary machinery.