# Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification

arXiv: https://arxiv.org/abs/2408.12426 · HTML: https://mlsanity.com/paper/2408.12426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 65 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2303.04129/) (2023)
- [Foundation Model is Efficient Multimodal Multitask Model Selector](https://mlsanity.com/paper/2308.06262/) (2023)
- [Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery](https://mlsanity.com/paper/2303.15975/) (2023)
- [Histogram binning revisited with a focus on human perception](https://mlsanity.com/paper/2109.06612/) (2021)
- [Exploring Self-Attention for Crop-type Classification Explainability](https://mlsanity.com/paper/2210.13167/) (2022)
- [SIFT and SURF based feature extraction for the anomaly detection](https://mlsanity.com/paper/2203.13068/) (2022)
- [Action Recognition with Image Based CNN Features](https://mlsanity.com/paper/1512.03980/) (2015)

## Built on by (most cited first, 2 of 2)

- [SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases](https://mlsanity.com/paper/2508.17107/) (2025)
- [AgrI Challenge: A Data-Centric AI Competition for Cross-Team Validation in Agricultural Vision](https://mlsanity.com/paper/2603.07356/) (2026)

## Abstract (arXiv)

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.