Agricultural applications supported with information technologies increase plant production, protect soil and reduce labor, which is crucial for sustainable agriculture. Impacts of planting dates on production are very well known. In the current study machine learning algorithms have been used in determining planting date. The proposed method aims to help farmers to obtain higher yield providing them with accurate planting date. For this purpose, metereological data was used as an input. For each year, metereological information (Daily Maximum Air Temperature, Daily Relative Humudity, Daily Average Air Temperature, Daily Minimum Air Temperature and Daily Precipitation) in the first 300 days were used to determine three different planting dates; early, normal and late for wheat crop. For estimation of planting date, classification algorithms of k Nearest Neighbor (kNN), Support Vector Machine (SVM) and Decisions Trees were used. Performances of different algorithms were calculated with leave one out cross validation approach. In order to eleminate extremely high processing time because of high dimension of the data set and improve estimation performance, genetic algorithm was used to reduce the number of features. For the estimations performed using both all features and also the features selected with genetic algorithm the highest accuracies were obtained using kNN method with classification accuracy rates of 37% and 92%, respectively. Overall, the results showed that wheat planting date could be determined successfully from climate information obtained in the first 300 days with the help of machine learning techniques combined with feature selection using genetic algorithm, which will prevent low productivity, financial and labor loss as a result of inaccurate planting date.

信息技术支持的农业应用提高了植物产量，保护了土壤并减少了劳动力，这对于可持续农业至关重要。播种日期对生产的影响是众所周知的。在当前的研究中，机器学习算法已用于确定种植日期。提出的方法旨在帮助农民获得更高的产量，为其提供准确的播种日期。为此，将计量学数据用作输入。每年，前300天的气象信息（每日最高气温，每日相对湿度，每日平均气温，每日最低气温和每日降水）用于确定三个不同的播种日期。小麦作物早，正常和晚。为了估算播种日期，使用了k最近邻（kNN），支持向量机（SVM）和决策树的分类算法。采用留一法交叉验证的方法来计算不同算法的性能。为了消除由于数据集的高维而导致的极高的处理时间并提高估计性能，使用了遗传算法来减少特征数量。对于使用所有特征以及通过遗传算法选择的特征进行的估计，使用kNN方法获得的最高准确度分别为37％和92％。总体，