Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data

Furuya, Danielle Elis Garcia; Ma, Lingfei; Pinheiro, Mayara Maezano Faita; Gomes, Felipe David Georges; Gonçalvez, Wesley Nunes; Marcato, José; Rodrigues, Diego de Castro; Blassioli‐Moraes, Maria Carolina; Michereff, Mirian Fernandes Furtado; Borges, Miguel; Alaumann, Raúl Alberto; Ferreira, Ednaldo José; Osco, Lucas Prado; Ramos, Ana Paula Marques; Li, Jonathan; Jorge, Lúcio André de Castro

doi:10.1016/j.jag.2021.102608

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…"Dalam pertumbuhannya tanaman jagung peka dengan serangan hama dan penyakit. Hama yang sering menyerang tanaman jagung yaitu ; ulat grayak Spodoptera sp., (Furuya et al, 2021;Herlinda et al, 2022), penggerek batang jagung (Ostrinia furnacalis), belalang kembara (Locusta sp. ), dan kutu daun (Aphis sp.),.…”

Section: Metode Penelitianunclassified

Pengembangan Aplikasi Sistem Pakar Deteksi Dini Hama dan Penyakit Tanaman Jagung

Lihawa,

Ilahude,

Latief

et al. 2024

JPPT

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Hama dan penyakit tanaman jagung berpotensi menimbulkan gagal panen. Kurangnya informasi dan pengetahuan mengenai serangan hama penyakit tanaman serta terbatasnya penyuluh lapangan menyebabkan kesalahan dalam diagnosa jenis hama dan penyakit tanaman jagung. Hal ini berakibat pada pengelolaan tanaman yang kurang tepat sehingga produksi menurun. Oleh karena itu, petani membutuhkan sebuah alat bantu untuk mendeteksi serangan hama dan penyakit melalui gejala fisik yang terlihat pada tanaman di lapangan. Penelitian ini bertujuan menghasilkan Sistem Informasi Pertanian (SIP) yaitu sistem pakar berbasis android untuk mendeteksi hama dan penyakit pada tanaman jagung. Metode yang digunakan adalah perangkat lunak dengan prototyping untuk mendapatkan gambaran aplikasi yang akan dibangun melalui rancangan aplikasi prototype kemudian dievaluasi oleh user. Tahapan penelitian meliputi ; pengumpulan kebutuhan, membangun prototype, evaluasi protoype, pengkodean system, menguji system, evaluasi system, dan penggunaan system. Hasilnya adalah gejala serangan hama dan penyakit pada tanaman jagung di lapangan, dapat terdeteksi melalui bentuk gejala yang ada dicocokkan dengan gambar dan karakteristik gejala yang ditampilkan pada software SIP yang terinstal pada handphone atau Tablet berbasis android. Hasil pengujian dari 28 responden menunjukkan bahwa tingkat keberhasilan deteksi gejala hama pada tanaman jagung sebesar 75%, dan untuk gejala penyakit sebesar 90%.

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Section: Metode Penelitianunclassified

Pengembangan Aplikasi Sistem Pakar Deteksi Dini Hama dan Penyakit Tanaman Jagung

Lihawa,

Ilahude,

Latief

et al. 2024

JPPT

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“…Like many invasive weeds, climbing fern grows in extremely remote and difficult to access areas, and remote detection would be far more efficient. Detecting insect herbivory from UAS equipped with high-resolution cameras is currently a major avenue of research within agroecosystems (Garcia Furuya et al 2021; Tetila et al 2020). The IPRL, ISPHRU, and PMRU are continuing to develop university and industry partnerships to expand the detection capabilities with UAS and provide more information for training artificial intelligence (AI).…”

Section: Present—ars Weed Science Researchmentioning

confidence: 99%

Agricultural Research Service Weed Science Research: Past, Present, and Future

Young¹,

Anderson²,

Baerson

et al. 2023

Weed Sci

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The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

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“…Here, we conducted one experiment with a highly redundant and complex dataset aiming to solve an agricultural-related problem. As such, while the practical value of said task was already discussed in previous research [18,8], it is still important to note that the appropriate approach to deal with these datasets necessitates a critical investigation. This paper aimed to highlight some of these aspects.…”

Section: Tablementioning

confidence: 99%

An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods

Osco

Furuya

et al. 2022

Infrared Physics & Technology

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Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data

Cited by 9 publications

References 31 publications

Pengembangan Aplikasi Sistem Pakar Deteksi Dini Hama dan Penyakit Tanaman Jagung

Pengembangan Aplikasi Sistem Pakar Deteksi Dini Hama dan Penyakit Tanaman Jagung

Agricultural Research Service Weed Science Research: Past, Present, and Future

An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods

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