Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases

Aparecido, Lucas Eduardo de Oliveira; Rolim, Glauco de Souza; Moraes, José Reinaldo da Silva Cabral de; Costa, Cícero Teixeira Silva; Souza, Paulo Sergio de

doi:10.1007/s00484-019-01856-1

Cited by 52 publications

(31 citation statements)

References 43 publications

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“…To execute precision farming, it is necessary for these tools to be able to process and make inferences from collected data, like an expert in the fields. Image processing integrated with machine learning is extensively applied in precision agriculture and has gained wide attention in the field of detecting plant diseases [2][3][4][5][6], weeds [7][8][9][10][11], and pests [12][13][14][15][16]. Ernest et al [2] employed a linear support vector classifier and the k-nearest neighbor algorithm to diagnose diseases from plant images.…”

Section: Introductionmentioning

confidence: 99%

“…Ahmed et al [8] employed SVMs to recognize six species of weeds from images, achieving 97.3% accuracy by combining the extractors. Ebrahimi et al [12] combined histogram equalization with the SVM model for detecting the pest diseases of whitefly and housefly in a strawberry greenhouse environment [13]. Lucas et al [13] used multiple machine learning algorithms of multiple linear regression, K-neighbors regressor, random forest regressor, and artificial neural network in pest warning systems, which could improve the efficiency of the chemical control of pest diseases on coffee tree.…”

Section: Introductionmentioning

confidence: 99%

“…Ebrahimi et al [12] combined histogram equalization with the SVM model for detecting the pest diseases of whitefly and housefly in a strawberry greenhouse environment [13]. Lucas et al [13] used multiple machine learning algorithms of multiple linear regression, K-neighbors regressor, random forest regressor, and artificial neural network in pest warning systems, which could improve the efficiency of the chemical control of pest diseases on coffee tree. Although machine learning has been successfully applied in agriculture fields, the limitation of image preprocessing and extracting the features in advance greatly affects recognition results.…”

Section: Introductionmentioning

confidence: 99%

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A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

et al. 2021

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Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, resulting in extreme crop losses. The species of mealybugs, Coccidae, and Diaspididae, which are the primary pests of the scale insect in Taiwan, can not only lead to serious damage to the plants but also severely affect agricultural production. Hence, to recognize the scale pests is an important task in Taiwan’s agricultural field. In this study, we propose an AI-based pest detection system for solving the specific issue of detection of scale pests based on pictures. Deep-learning-based object detection models, such as faster region-based convolutional networks (Faster R-CNNs), single-shot multibox detectors (SSDs), and You Only Look Once v4 (YOLO v4), are employed to detect and localize scale pests in the picture. The experimental results show that YOLO v4 achieved the highest classification accuracy among the algorithms, with 100% in mealybugs, 89% in Coccidae, and 97% in Diaspididae. Meanwhile, the computational performance of YOLO v4 has indicated that it is suitable for real-time application. Moreover, the inference results of the YOLO v4 model further help the end user. A mobile application using the trained scale pest recognition model has been developed to facilitate pest identification in farms, which is helpful in applying appropriate pesticides to reduce crop losses.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

et al. 2021

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“…Feature selection concerns the process of reducing a large number of features in order to identify those that contribute most to the prediction of the variable or output under examination. To this end, some papers used Pearson correlation [37,53], Principal Component Analysis (PCA) [38,41,46,52,54,55], and Linear Discriminant Analysis (LDA) [54]. Other operations involved different data balancing techniques.…”

Section: Pre-processingmentioning

confidence: 99%

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Fenu

Malloci

2021

BDCC

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Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

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“…The Artificial Neural Network (ANN) is a promising and effective tool for non-linear modeling and complex time-series. It has been used in different fields of science such as medicine (Muhammad et al, 2019), hydrology (Asadi et al, 2019), and agriculture (De Oliveira Aparecido et al, 2020). The ANN model is a mathematical model in which the architecture is analogous to brain functioning.…”

Section: Introductionmentioning

confidence: 99%

Air temperature estimation techniques in Minas Gerais state, Brazil, Cwa and Cwb climate regions according to the Köppen-Geiger climate classification system

et al. 2021

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Air temperature significantly affects the processes involving agricultural and human activities. The knowledge of the temperature of a given location is essential for agricultural planning. It also helps to make decisions regarding human activities. However, it is not always possible to determine this variable. It is necessary to make a precise estimate, using methods that are capable of detecting the existing variations. The aim of this study was to develop models of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) to estimate the mean (Tmean), maximum (Tmax), and minimum (Tmin) monthly air temperatures as a function of geographic coordinates and altitude for different localities in Minas Gerais state, Brazil, with climatic classification Cwa or Cwb. The average monthly data (Tmean, Tmax, and Tmin), over a period of 30 years, were collected from 20 climatological stations. The MLR was able to estimate the Tmax with accuracy. However, the predictive capacity of estimating Tmean and Tmin was low. The algorithms RF and ANN were used to estimate Tmean, Tmax, and Tmin with high accuracy. The best results were obtained using the RF model.

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Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases

Cited by 52 publications

References 43 publications

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Air temperature estimation techniques in Minas Gerais state, Brazil, Cwa and Cwb climate regions according to the Köppen-Geiger climate classification system

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