Topic
Yield mapping
About: Yield mapping is a research topic. Over the lifetime, 280 publications have been published within this topic receiving 5059 citations.
Papers published on a yearly basis
Papers
1 May 2017
TL;DR: In this article, the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples, was presented.
Abstract: An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100–1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of > 0.9 achieved for apples and mangoes.
457 citations
TL;DR: Convolutional Neural Networks – a deep learning methodology showing outstanding performance in image classification tasks – are applied to build a model for crop yield prediction based on NDVI and RGB data acquired from UAVs and significantly, the CNN architecture performed better with RGB data than the NDVI data.
448 citations
TL;DR: This work is a pioneer to create a multi-labeled and knowledge-based outdoor orchard image library using 4000 images in the real world and improvement of the convolutional and pooling layers is achieved to have a more accurate and faster detection.
398 citations
TL;DR: In this article, the use of Google Earth Engine (GEE) was used to build a 10"m resolution map of cropland presence, maize presence, and maize yields for the main 2017 maize season in Kenya and Tanzania.
312 citations
TL;DR: The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or L STM model in both end-of-season and in-season soybean yield prediction in CONUS at the county-level.
Abstract: Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.
297 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2022 | 1 |
| 2021 | 13 |
| 2020 | 17 |
| 2019 | 16 |
| 2018 | 10 |
| 2017 | 13 |