Jabed Md Abu, Azmi Murad Masrah Azrifah
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia.
Department of Computer Science and Engineering, University of Creative Technology Chittagong, Chattogram, Bangladesh.
Heliyon. 2024 Nov 29;10(24):e40836. doi: 10.1016/j.heliyon.2024.e40836. eCollection 2024 Dec 30.
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas. The review identifies a wealth of AI-powered solutions employed in crop yield prediction, emphasizing the importance of precise environmental and agricultural data. Key factors contributing to accurate estimation include temperature, rainfall, soil type, humidity, and various vegetation indices, such as NDVI, EVI, LAI, and NDWI. The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). In the realm of deep learning, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Deep Neural Networks (DNN) emerge as promising candidates. The findings of this study shed light on the transformative potential of advanced AI techniques in improving crop yield estimation accuracy, ultimately enhancing agricultural planning and resource management. By addressing the challenges posed by geographical diversity, crop heterogeneity, and changing environmental conditions, AI-driven models offer new avenues for sustainable agriculture in an ever-evolving world. This research paper provides valuable insights and directions for future studies, highlighting the critical role of AI in ensuring food security and sustainability in agriculture.
在追求精确作物产量估计的过程中，农业部门面临着诸多挑战，而精确的作物产量估计对于高效资源管理以及缓解全球人口快速增长带来的粮食短缺至关重要。本研究论文深入探讨了先进人工智能（AI）技术在应对各种农业挑战背景下提高作物产量估计的应用。通过系统的文献综述和对相关研究的分析，本文探讨了人工智能方法，如机器学习（ML）和深度学习（DL），在应对地理差异、作物多样性和种植区域所带来的复杂性方面的作用。该综述确定了大量用于作物产量预测的人工智能驱动的解决方案，强调了精确的环境和农业数据的重要性。有助于精确估计的关键因素包括温度、降雨量、土壤类型、湿度以及各种植被指数，如归一化植被指数（NDVI）、增强植被指数（EVI）、叶面积指数（LAI）和归一化水指数（NDWI）。该研究论文还研究了机器学习领域经常使用的算法，包括随机森林（RF）、人工神经网络（ANN）和支持向量机（SVM）。在深度学习领域，卷积神经网络（CNN）、长短期记忆网络（LSTM）和深度神经网络（DNN）成为有前景的候选方法。本研究的结果揭示了先进人工智能技术在提高作物产量估计准确性方面的变革潜力，最终加强农业规划和资源管理。通过应对地理多样性、作物异质性和不断变化的环境条件带来的挑战，人工智能驱动的模型为不断发展的世界中的可持续农业提供了新途径。本研究论文为未来研究提供了有价值的见解和方向，突出了人工智能在确保农业粮食安全和可持续性方面的关键作用。
