Convolutional Neural Networks (CNNs) in Agriculture

This repository lists reviewed literature papers on the topic of CNNs in Agriculture. Contributions are open, please report any technical problem or expired/invalid link.

How to cite

If this work helped you, please cite (link and link):

@article{el2024images,
  title={Images and CNN applications in smart agriculture},
  author={El Sakka, Mohammad and Mothe, Josiane and Ivanovici, Mihai},
  journal={European Journal of Remote Sensing},
  volume={57},
  number={1},
  pages={2352386},
  year={2024},
  publisher={Taylor \& Francis}
}

@article{el2025review,
  title={A Review of CNN Applications in Smart Agriculture Using Multimodal Data},
  author={El Sakka, Mohammad and Ivanovici, Mihai and Chaari, Lotfi and Mothe, Josiane},
  journal={Sensors},
  volume={25},
  number={2},
  pages={472},
  year={2025},
  publisher={MDPI}
}

Weed Detection

• Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming (2018) link
• Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density (2019) link
• Weed classification in grasslands using convolutional neural networks (2019) link
• A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images (2020) link
• Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields (2020) link
• Deep Convolutional Neural Networks for Weed Detection in Agricultural Crops Using Optical Aerial Images (2020) link
• Towards weeds identification assistance through transfer learning (2020) link
• Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network (2020) link
• A Patch-image Based Classification Approach for Detection of Weeds in Sugar beet Crop (2021) link
• Accuracy and Efficiency Comparison of Object Detection Open-Source Models (2021) link
• Transferable Convolutional Neural Network for Weed Mapping With Multisensor Imagery (2021) link
• Detection of Weeds Growing in Alfalfa Using Convolutional Neural Networks (2022) link
• Classification of paddy crop and weeds using semantic segmentation (2022) link
• CNN Based Automated Weed Detection System Using UAV Imagery. (2022) link
• Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion (2022) link
• Evaluation of convolutional neural networks for herbicide susceptibility-based weed detection in turf (2023) link
• Instance segmentation method for weed detection using UAV imagery in soybean fields (2023) link
• Performance evaluation of deep learning object detectors for weed detection for cotton (2023) link
• Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images (2023) link
• Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-Net (2023) link
• UAV-based weed detection in Chinese cabbage using deep learning (2023) link
• Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm (2023) link
• Small-target weed-detection model based on YOLO-V4 with improved backbone and neck structures (2023) link
• Maize seedling detection under different growth stages and complex field environments based on an improved Faster R--CNN (2019) link
• Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems (2022) link
• Transfer learning for the classification of sugar beet and volunteer potato under field conditions (2018) link

Crop Disease Detection

• A Novel Method of Plant Leaf Disease Detection Based on Deep Learning and Convolutional Neural Network (2021) link
• Disease Detection in Apple Leaves Using Deep Convolutional Neural Network (2021) link
• A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection (2022) link
• An Improved Convolutional Neural Network for Plant Disease Detection Using Unmanned Aerial Vehicle Images (2022) link
• Banana Plant Disease Classification Using Hybrid Convolutional Neural Network (2022) link
• Comparative Evaluation of the Convolutional Neural Network based Transfer Learning Models for Classification of Plant Disease (2022) link
• Modeling Convolutional Neural Network for Detection of Plant Leaf Spot Diseases (2022) link
• A novel multi-head CNN design to identify plant diseases using the fusion of RGB images (2023) link
• Comparison of CNN-based deep learning architectures for rice diseases classification (2023) link
• DeepTuber: Sequential CNN-based Disease Detection in Potato Plants for Enhanced Crop Management (2023) link
• LightMixer: A novel lightweight convolutional neural network for tomato disease detection (2023) link
• VGG-ICNN: A Lightweight CNN model for crop disease identification (2023) link
• Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images (2023) link
• A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information (2024) link
• Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data (2024) link
• Improving Wheat Leaf Disease Image Classification with Point Rend Segmentation Technique (2024) link
• Neural Network-Based Stress Detection in Crop Multispectral Imagery for Precision Agriculture (2024) link
• Performance analysis of segmentation models to detect leaf diseases in tomato plant | Multimedia Tools and Applications (2024) link
• Tomato Disease Detection Using Multispectral Imaging with Deep Learning Models (2024) link
• Towards Accurate Disease Segmentation in Plant Images: A Comprehensive Dataset Creation and Network Evaluation (2024) link
• UAV T-YOLO-Rice: An Enhanced Tiny Yolo Networks for Rice Leaves Diseases Detection in Paddy Agronomy | IEEE Journals & Magazine | IEEE Xplore (2024) link
• Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease (2022) link
• The Convolutional Neural Network for Plant Disease Detection Using Hierarchical Mixed Pooling Technique with Smoothing to Sharpening Approach (2023) link

Crop Classification

• Fruit Classification Based on Six Layer Convolutional Neural Network (2018) link
• Analysis of transfer learning for deep neural network based plant classification models (2019) link
• Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China (2019) link
• Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data (2021) link
• An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network (2022) link
• Optimal deep convolutional neural network based crop classification model on multispectral remote sensing images (2022) link
• Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series (2022) link
• A framework base on deep neural network (DNN) for land use land cover (LULC) and rice crop classification without using survey data (2023) link
• Extracting Tea Plantations from Multitemporal Sentinel-2 Images Based on Deep Learning Networks (2023) link
• Crop classification methods and influencing factors of reusing historical samples based on 2D-CNN (2023) link
• Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms (2023) link
• Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine (2023) link
• Wheat crop classification using deep learning (2024) link
• Crop classification of multitemporal PolSAR based on 3-D attention module with ViT (2023) link
• Convolutional Neural Network (CNN) for Crop-Classification of Drone Acquired Hyperspectral Imagery (2022) link

Water Management

• Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations (2018) link
• Integration of Convolutional Neural Network and Thermal Images into Soil Moisture Estimation (2018) link
• SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING (2018) link
• Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning (2019) link
• Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain (2019) link
• Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing (2020) link
• Learned features of leaf phenotype to monitor maize water status in the fields (2020) link
• Prediction of reference evapotranspiration for irrigation scheduling using machine learning (2020) link
• A cloud computing-based approach using the visible near-infrared spectrum to classify greenhouse tomato plants under water stress (2021) link
• Classification of water stress in cultured Sunagoke moss using deep learning (2021) link
• CNN Based Water Stress Detection in Chickpea Using UAV Based Hyperspectral Imaging (2021) link
• CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China (2021) link
• Intelligent Monitoring of Stress Induced by Water Deficiency in Plants using Deep Learning (2021) link
• Performance Analysis of CNN, AlexNet and VGGNet Models for Drought Prediction using Satellite Images (2021) link
• Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks (2021) link
• Characterizing Water Deficiency induced stress in Plants using Gabor filter based CNN (2022) link
• Lightweight deep CNN models for identifying drought stressed plant (2022) link
• Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit (2022) link
• An augmented attention-based lightweight CNN model for plant water stress detection (2023) link
• Early detection of drought stress in tomato from spectroscopic data: A novel convolutional neural network with feature selection (2023) link
• Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery (2023) link
• Estimation of soil moisture in drip-irrigated citrus orchards using multi-modal UAV remote sensing (2024) link

Yield Prediction

• Apple Counting using Convolutional Neural Networks (2018) link
• Crop Yield Prediction by Modified Convolutional Neural Network and Geographical Indexes (2018) link
• County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019) link
• Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages (2019) link
• Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest (2020) link
• Convolutional neural networks in predicting cotton yield from images of commercial fields (2020) link
• Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks (2021) link
• Estimation of corn yield based on hyperspectral imagery and convolutional neural network (2021) link
• Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process (2021) link
• Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning (2021) link
• Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal (2021) link
• Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning (2021) link
• Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches (2022) link
• SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques (2022) link
• Wheat head counting in the wild by an augmented feature pyramid networks-based convolutional neural network (2022) link
• Yield estimation of high-density cotton fields using low-altitude UAV imaging and deep learning (2022) link
• A Simple and Efficient Deep Learning Architecture for Corn Yield Prediction (2023) link
• CNN-BI-LSTM-CYP: A deep learning approach for sugarcane yield prediction (2023) link
• Detection of fruit maturity stage and yield estimation in wild blueberry using deep learning convolutional neural networks (2023) link
• Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing (2023) link
• Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data (2023) link
• Rice Yield Prediction in Hubei Province Based on Deep Learning and the Effect of Spatial Heterogeneity (2023) link
• Winter wheat yield prediction using convolutional neural networks and UAV-based multispectral imagery (2023) link
• End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images | Precision Agriculture (2024) link
• A Deep Learning Based Approach for Strawberry Yield Prediction via Semantic Graphics (2021) link
• Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: A comparison with traditional machine learning algorithms (2023) link
• Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models (2020) link
• Satellite images and deep learning tools for crop yield prediction and price forecasting (2021) link
• Use of deep neural networks for crop yield prediction: A case study of soybean yield in lauderdale county, alabama, usa (2019) link