Pest-infested Soybean Leaf Image Classification with Deep Learning Techniques for Integrated Pest Management (IPM)

Badgujar, Chetan; Mansur, Hasib; Flippo, Daniel

doi:10.13031/aim.202201096

Cited by 3 publications

(4 citation statements)

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“…Badgujar, Mansur & Flippo [23] already used the same dataset as in this study. For the annual international meeting of the American Society of Agricultural and Biological Engineers, they produced an overview of classifying images of soybean leaves infested with pests using deep learning techniques.…”

Section: B Related Workmentioning

confidence: 99%

“…Previous work demonstrated the successful use of deep learning models in the detection of various plant pests or plant diseases [2], [6], [18]- [23]. Specifically, Badgujar, Mansur & Flippo [23] have already used the same dataset and adopted a multiclass-approach to classify healthy soybeans, soybeans infested with Diabrotica speciosa and soybeans infested with caterpillars. We address this approach and improve its results.…”

Section: Introductionmentioning

confidence: 99%

“…• We develop a model to outperform the existing benchmark regarding the detection of soybeans infected with Caterpillar or Diabrotica speciosa by Badgujar, Mansur & Flippo [23]. • We adopt a transfer learning approach using the pretrained deep learning model VGG19.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

Farah,

Drack,

Dawel

et al. 2023

IEEE Access

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We address the soybean leaves infection problem by proposing a robust classification model that can reliably detect infests by Diabrotica speciosa and caterpillars. Our transfer-learning based model uses a VGG19 convolutional neural network to classify the soybean leaves and we achieve balanced accuracies between 93.71 % and 94.16 % on unseen testing data, what sets a new benchmark and outperform previous work using the same dataset. Our work has theoretical and practical implications. The soybean plays a crucial role in the agricultural industry. Infestation of soybeans leads to enormous economic and environmental losses. With our model presented here, an early and accurate detection to control the spread of plant pests is possible, what reduces economic and ecological damages.

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Section: B Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

Farah,

Drack,

Dawel

et al. 2023

IEEE Access

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“…However, collecting accurate and timely information on pest infestation is a crucial aspect of integrated pest management. Moreover, several pests and diseases exhibit an uneven spatial distribution, with typical patch structures evolving around discrete foci (localized areas exhibiting symptoms), especially during the early stages of development [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Design of a Reconfigurable Crop Scouting Vehicle for Row Crop Navigation: A Proof-of-Concept Study

Schmitz

Badgujar

Mansur

et al. 2022

Sensors

Self Cite

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Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV’s reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system.

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The Impact of Permethrin and Cypermethrin on Plants, Soil Enzyme Activity, and Microbial Communities

Borowik

Wyszkowska

Zaborowska

et al. 2023

IJMS

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Pyrethroids are insecticides most commonly used for insect control to boost agricultural production. The aim of the present research was to determine the effect of permethrin and cypermethrin on cultured and non-cultivated bacteria and fungi and on the activity of soil enzymes, as well as to determine the usefulness of Zea mays in mitigating the adverse effects of the tested pyrethroids on the soil microbiome. The analyses were carried out in the samples of both soil not sown with any plant and soil sown with Zea mays. Permethrin and cypermethrin were found to stimulate the multiplication of cultured organotrophic bacteria (on average by 38.3%) and actinomycetes (on average by 80.2%), and to inhibit fungi growth (on average by 31.7%) and the enzymatic activity of the soil, reducing the soil biochemical fertility index (BA) by 27.7%. They also modified the number of operational taxonomic units (OTUs) of the Actinobacteria and Proteobacteria phyla and the Ascomycota and Basidiomycota phyla. The pressure of permethrin and cypermethrin was tolerated well by the bacteria Sphingomonas (clone 3214512, 1052559, 237613, 1048605) and Bacillus (clone New.ReferenceOTU111, 593219, 578257), and by the fungi Penicillium (SH1533734.08FU, SH1692798.08FU) and Trichocladium (SH1615601.08FU). Both insecticides disturbed the growth and yielding of Zea mays, as a result of which its yield and leaf greenness index decreased. The cultivation of Zea mays had a positive effect on both soil enzymes and soil microorganisms and mitigated the anomalies caused by the tested insecticides in the microbiome and activity of soil enzymes. Permethrin decreased the yield of its aerial parts by 37.9% and its roots by 33.9%, whereas respective decreases caused by cypermethrin reached 16.8% and 4.3%.

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Pest-infested Soybean Leaf Image Classification with Deep Learning Techniques for Integrated Pest Management (IPM)

Cited by 3 publications

References 0 publications

A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

Design of a Reconfigurable Crop Scouting Vehicle for Row Crop Navigation: A Proof-of-Concept Study

The Impact of Permethrin and Cypermethrin on Plants, Soil Enzyme Activity, and Microbial Communities

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