Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data

Abstract

Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth’s surface imagery.

DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling

“…Our approach is related to work in active learning (Settles 2009) and semi-supervised learning (Chapelle, Scholkopf, and Zien 2009), where the goal is to reduce human labeling effort to learn models that generalize on i.i.d. held out data.…”

“…Many applications of AI-especially to science, society, and the environment-use computer vision to detect and count objects in massive image collections. Examples include wildlife population monitoring (Wu et al 2023) and the mapping of agriculture (Singh et al 2022;Turkoglu et al 2021) or poverty (Ayush et al 2021;Yeh et al 2020) from satellite images. We are interested in two particular applications: (1) counting bird roosts in radar to understand population responses to climate change and aid conservation, and (2) counting damaged buildings in satellite images to inform disaster response.…”

A Review of Machine Learning Techniques in Agroclimatic Studies

“…Agricultural Application [18,26] Decision Tree Crop Yield Prediction, Disease Detection, Soil Assessment [18][19][20] Random Forest Crop Yield Prediction, Disease Detection, Soil Assessment [18,27] Extreme Gradient Boosting Crop Yield Prediction, Soil Assessment [18,20] Naive Bayes Crop Yield Prediction, Disease Detection [18,21] K-Nearest Neighbors Crop Yield Prediction, Disease Detection [28] Ensemble Traditional ML Models Crop Yield Prediction [26] Multi-Linear Regressor Crop Yield Prediction [29] RNN Crop Yield Prediction [29] LSTM Crop Yield Prediction [29] Support Vector Regression Crop Yield Prediction [23,24,30,31] CNN Crop Yield Prediction, Disease Detection [30] GNN Crop Yield Prediction [30] U-Net Crop Yield Prediction [23,25,32] ANN Crop Yield Prediction, Disease Detection [25] DBSCAN Crop Yield Prediction [23,25] Support Vector Machine Crop Yield Prediction, Disease Detection, Smart Farming [33] Vision Transformers Disease Detection [22] VGG-RNN Hybrid Soil Assessment [23,24] MLP Soil Assessment…”

“…Moreover, for some papers that did provide certain details, the information concerning the model was often insufficient for result reproduction [19,26,30,32,68]. A substantial inconsistency was observed regarding metric selection, with many papers comparing their results to randomly chosen algorithms.…”

Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka

“…ANN has been used to classify various types of remotely sensed data and produced results better than those of traditional statistical classification methods (Jiang et al, 2004). In recent years, several researchers have used the Convolutional Neural Networks (CNN) for performing object detection, image segmentation and pattern recognition (Nataliia et al, 2017;Verma and Jana, 2019;Chouhan et al, 2022;Singh et al, 2022) and for mapping Rabi crops using multispectral temporal images of Sentinel-2A/2B sensors (Snighal et al, 2022). CNN is a deep learning algorithm and it considers shape, texture, size, and spatial relationships of objects to extract information from images.…”