The increasing popularity of Artificial Intelligence in recent years has led to a surge in interest in image classification, especially in the agricultural sector. With the help of Computer Vision, Machine Learning, and Deep Learning, the sector has undergone a significant transformation, leading to the development of new techniques for crop classification in the field. Despite the extensive research on various image classification techniques, most have limitations such as low accuracy, limited use of data, and a lack of reporting model size and prediction. The most significant limitation of all is the need for model explainability. This research evaluates four different approaches for crop classification, namely traditional ML with handcrafted feature extraction methods like SIFT, ORB, and Color Histogram; Custom Designed CNN and established DL architecture like AlexNet; transfer learning on five models pre-trained using ImageNet such as EfficientNetV2, ResNet152V2, Xception, Inception-ResNetV2, MobileNetV3; and cutting-edge foundation models like YOLOv8 and DINOv2, a self-supervised Vision Transformer Model. All models performed well, but Xception outperformed all of them in terms of generalization, achieving 98% accuracy on the test data, with a model size of 80.03 MB and a prediction time of 0.0633 seconds. A key aspect of this research was the application of Explainable AI to provide the explainability of all the models. This journal presents the explainability of Xception model with LIME, SHAP, and GradCAM, ensuring transparency and trustworthiness in the models' predictions. This study highlights the importance of selecting the right model according to task-specific needs. It also underscores the important role of explainability in deploying AI in agriculture, providing insightful information to help enhance AI-driven crop management strategies.

翻译：近年来人工智能的日益普及引发了图像分类研究的热潮，尤其在农业领域。借助计算机视觉、机器学习和深度学习技术，该领域经历了重大变革，推动了田间作物分类新方法的发展。尽管已有大量针对各类图像分类技术的研究，但大多数方法仍存在准确率低、数据利用有限、未报告模型规模与预测耗时等局限。其中最为突出的限制在于模型可解释性的需求。本研究评估了四种不同的作物分类方法：采用SIFT、ORB和颜色直方图等手工特征提取的传统机器学习方法；定制设计的CNN与AlexNet等成熟深度学习架构；基于EfficientNetV2、ResNet152V2、Xception、Inception-ResNetV2、MobileNetV3等五种ImageNet预训练模型的迁移学习方法；以及YOLOv8和自监督视觉Transformer模型DINOv2等前沿基础模型。所有模型均表现良好，其中Xception在泛化能力方面表现最优，在测试数据上达到98%的准确率，模型规模为80.03 MB，预测时间为0.0633秒。本研究的关键在于应用可解释人工智能技术为所有模型提供可解释性。本文通过LIME、SHAP和GradCAM方法展示了Xception模型的可解释性，确保了模型预测的透明度和可信度。本研究强调了根据具体任务需求选择合适模型的重要性，同时阐明了可解释性在农业人工智能部署中的关键作用，为增强人工智能驱动的作物管理策略提供了深刻见解。