Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification
- URL: http://arxiv.org/abs/2408.12426v1
- Date: Thu, 22 Aug 2024 14:20:34 GMT
- Title: Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification
- Authors: Sudi Murindanyi, Joyce Nakatumba-Nabende, Rahman Sanya, Rose Nakibuule, Andrew Katumba,
- Abstract summary: 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.
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.
- Score: 0.49110747024865004
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream [3.4526439922541705]
We evaluate scaling laws for modeling the primate visual ventral stream (VVS)
We observe that while behavioral alignment continues to scale with larger models, neural alignment saturates.
Increased scaling is especially beneficial for higher-level visual areas, where small models trained on few samples exhibit only poor alignment.
arXiv Detail & Related papers (2024-11-08T17:13:53Z) - Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading [11.220653004059304]
We propose a novel classifi-cation method based on deep learning, namely GSNets, which can effectively introduce guided self-attention for classifying grain size.
Experiments show that our GSNet yields a classifi-cation accuracy of 90.1%, surpassing the state-of-the-art Swin Transformer V2 by 1.9%.
We intuitively believe our approach is applicable to broader ap-plications like object detection and semantic segmentation.
arXiv Detail & Related papers (2024-10-08T07:40:31Z) - Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
We develop an efficient, autoregression-based vision model on a limited dataset.
We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding.
Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
arXiv Detail & Related papers (2024-02-07T13:41:53Z) - Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
We introduce a novel data-model co-design perspective: to promote superior weight sparsity.
Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
arXiv Detail & Related papers (2023-12-03T13:50:24Z) - Metric Tools for Sensitivity Analysis with Applications to Neural
Networks [0.0]
Explainable Artificial Intelligence (XAI) aims to provide interpretations for predictions made by Machine Learning models.
In this paper, a theoretical framework is proposed to study sensitivities of ML models using metric techniques.
A complete family of new quantitative metrics called $alpha$-curves is extracted.
arXiv Detail & Related papers (2023-05-03T18:10:21Z) - Facilitated machine learning for image-based fruit quality assessment in
developing countries [68.8204255655161]
Automated image classification is a common task for supervised machine learning in food science.
We propose an alternative method based on pre-trained vision transformers (ViTs)
It can be easily implemented with limited resources on a standard device.
arXiv Detail & Related papers (2022-07-10T19:52:20Z) - Revisiting Classifier: Transferring Vision-Language Models for Video
Recognition [102.93524173258487]
Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research.
In this study, we focus on transferring knowledge for video classification tasks.
We utilize the well-pretrained language model to generate good semantic target for efficient transferring learning.
arXiv Detail & Related papers (2022-07-04T10:00:47Z) - Towards Open-World Feature Extrapolation: An Inductive Graph Learning
Approach [80.8446673089281]
We propose a new learning paradigm with graph representation and learning.
Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
arXiv Detail & Related papers (2021-10-09T09:02:45Z) - ALT-MAS: A Data-Efficient Framework for Active Testing of Machine
Learning Algorithms [58.684954492439424]
We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data.
The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
arXiv Detail & Related papers (2021-04-11T12:14:04Z) - Improving the Performance of Fine-Grain Image Classifiers via Generative
Data Augmentation [0.5161531917413706]
We develop Data Augmentation from Proficient Pre-Training of Robust Generative Adrial Networks (DAPPER GAN)
DAPPER GAN is an ML analytics support tool that automatically generates novel views of training images.
We experimentally evaluate this technique on the Stanford Cars dataset, demonstrating improved vehicle make and model classification accuracy.
arXiv Detail & Related papers (2020-08-12T15:29:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.