UAV-based coffee yield prediction utilizing feature selection and deep learning

Barbosa, Brenon Diennevan Souza; Ferraz, Gabriel Araújo e Silva; Costa, Lucas; Ampatzidis, Yiannis; Vijayakumar, V.; Santos, Luana Maria dos

doi:10.1016/j.atech.2021.100010

Cited by 25 publications

(27 citation statements)

References 46 publications

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“…The labelling boxes are rectangles surrounding the ROIs. The image annotation information of the flower clusters and flushes is saved in VOC format, and the corresponding XML files are generated after the labelling procedure ( Li et al, 2022 ; Lin Y. et al, 2022 ). The XML file contains the image storage information, image name, annotation name and coordinate information of each labelled rectangle box.…”

Section: Methodsmentioning

confidence: 99%

“…To accurately measure the yields of fruit trees, in recent years, some researchers have used UAVs to take fruit images and have combined them with deep learning methods to detect the number of fruits ( Barbosa et al, 2021 ; Zhou X. et al, 2021 ). Apolo-Apolo et al (2020) used images captured by a UAV to monitor a total of 20 commercial citrus trees and then developed an automated image processing methodology to detect, count and estimate the sizes of citrus fruits on individual trees using deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Using the You Only Look Once version 2 (YOLOv2) model to conduct training and testing on the mango dataset, the detection precision reached 96.1%, so this approach could estimate the number of mango fruits. Li D. et al (2021) proposed a scheme based on UAV images and the YOLOv4 model to detect and locate suitable picking points on the fruiting branches of longan. The above studies show that methods based on UAVs and deep learning can solve related problems in agriculture.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Lin

Jia

et al. 2022

Front. Plant Sci.

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Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Lin

Jia

et al. 2022

Front. Plant Sci.

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“…Faster R-CNN had the best performance and required the least amount of time to train. For multimodal data mining to be effective, it must be possible to consistently represent intermodality and cross-modality in the global space in which the data are integrated ( Mkhabela et al, 2011 ; Barbosa et al, 2021 ; Bronstein et al, 2010 ; Poria et al, 2017 ). Multimodal data fusion is a key component of this process.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Al-Adhaileh

Aldhyani

2022

PeerJ Computer Science

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Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Analyses of datasets for crop yields, temperature, and insecticides led to the development of the MLP models. The datasets are randomly divided into separate samples, 70% for training and 30% for testing. The best-performing MLP model is characterized by values of (R = 100%) and (R2 = 96.33) for predicting insecticides in the testing process. The temperature, insecticides, and rainfall were examined with different crop yields to confirm the effectiveness of these parameters for increasing product crop yields in Saudi Arabia; we found that these items had highest relationships. The average values are R = 98.20%, 96.50, and 99.14% with for the temperature, insecticides, and rainfall, respectively. Based on these findings, it appeared that each of the parameter categories that are considered (temperature, pesticides, and rainfall) had a similar contribution to the accuracy of anticipated yield projection.

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“…However, Rosa et al [12] obtained a non-conclusive result using the NDVI and its relationship with the LAI. Barbosa et al proposed another essential approach in [13], studying regression models on the basis of physiological characteristics, such as the height and diameter obtained from RGB images using UAVs for yield estimation, manually validating the results with the image. In this research, the total beans of the coffee tree were obtained by georeferencing each point with the GCP tool.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

2023

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Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield.

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UAV-based coffee yield prediction utilizing feature selection and deep learning

Cited by 25 publications

References 46 publications

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

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