Ramzan Shabana, Ali Basharat, Raza Ali, Hussain Ibrar, Fitriyani Norma Latif, Gu Yeonghyeon, Syafrudin Muhammad
Government Sadiq College Women University Bahawalpur, Bahawalpur, Pakistan.
Agronomic Research Station Bahawalpur, Bahawalpur, Pakistan.
PeerJ Comput Sci. 2024 Nov 29;10:e2478. doi: 10.7717/peerj-cs.2478. eCollection 2024.
A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings. Accurate crop prediction is mainly concerned with agricultural research, which plays a major role in selecting accurate crops based on environmental factors and soil characteristics. Recently, recommender systems (RS) have gained much attention and are being utilized in various fields such as e-commerce, music, health, text, movies etc. Machine learning techniques can help predict the crop accurately. We proposed an innovative artificial neural network (ANN) based crop prediction system (CPS) to address the farmer's issue. The parameters considered during sensor-based soil data collection for this study are nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, electrical conductivity, and soil texture. Python programming language is used to design and validate the proposed system. The accuracy and reliability of the proposed CPS are assessed by using accuracy, precision, recall, and F1-score. We also optimized the proposed CPS by performing a hyperparameter Optimization analysis of applied learning methods. The proposed CPS model accuracy for both real-time collected and state-of-the-art datasets is 99%. The experimental results show that our proposed solution assists farmers in selecting the accurate crop and producing at their best, increasing their profit.
蓬勃发展的农业系统是农业国家经济增长的基石。当农民在选择作物时不考虑环境因素和土壤特性时，他们的作物产量会显著降低。作物预测使农民能够选择能使作物产量和收益最大化的作物。准确的作物预测主要涉及农业研究，农业研究在根据环境因素和土壤特性选择合适的作物方面发挥着重要作用。近年来，推荐系统（RS）受到了广泛关注，并被应用于电子商务、音乐、健康、文本、电影等各个领域。机器学习技术有助于准确预测作物。我们提出了一种基于创新人工神经网络（ANN）的作物预测系统（CPS）来解决农民的问题。本研究在基于传感器的土壤数据收集过程中考虑的参数有氮、磷、钾、温度、湿度、pH值、降雨量、电导率和土壤质地。使用Python编程语言来设计和验证所提出的系统。通过准确率、精确率、召回率和F1分数来评估所提出的CPS的准确性和可靠性。我们还通过对应用学习方法进行超参数优化分析来优化所提出的CPS。所提出的CPS模型对实时收集的数据集和最新数据集的准确率均为99%。实验结果表明，我们提出的解决方案有助于农民选择合适的作物并实现最佳产量，从而增加他们的利润。
