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کیرتیواسان, ام., کوکیلاوانی, اس., شانتی, ام., دیباکاران, گا., پنگایار سلوِی, آر., مورُوگان, ام., ایلایاباراتی, تی., شانموگام, پی. اس., سلوِوا کومار, ام.. (1404). مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی. سامانه مدیریت نشریات علمی, (), -. doi: 10.22067/jam.2025.90762.1314
ام. کیرتیواسان; اس. کوکیلاوانی; ام. شانتی; گا. دیباکاران; آر. پنگایار سلوِی; ام. مورُوگان; تی. ایلایاباراتی; پی. اس. شانموگام; ام. سلوِوا کومار. "مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22067/jam.2025.90762.1314
کیرتیواسان, ام., کوکیلاوانی, اس., شانتی, ام., دیباکاران, گا., پنگایار سلوِی, آر., مورُوگان, ام., ایلایاباراتی, تی., شانموگام, پی. اس., سلوِوا کومار, ام.. (1404). 'مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22067/jam.2025.90762.1314
کیرتیواسان, ام., کوکیلاوانی, اس., شانتی, ام., دیباکاران, گا., پنگایار سلوِی, آر., مورُوگان, ام., ایلایاباراتی, تی., شانموگام, پی. اس., سلوِوا کومار, ام.. مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22067/jam.2025.90762.1314

مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی

ماشین های کشاورزی
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 شهریور 1404    اصل مقاله (821.39 K)
نوع مقاله: مقاله مروری انگلیسی
شناسه دیجیتال (DOI): 10.22067/jam.2025.90762.1314
نویسندگان
ام. کیرتیواسان1؛ اس. کوکیلاوانی* 1؛ ام. شانتی2؛ گا. دیباکاران1؛ آر. پنگایار سلوِی3؛ ام. مورُوگان4؛ تی. ایلایاباراتی4؛ پی. اس. شانموگام4؛ ام. سلوِوا کومار1
1مرکز پژوهش اقلیم‌کشاورزی، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
2مرکز مطالعات حفاظت گیاه، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
3گروه علوم فیزیکی و فناوری اطلاعات، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
4گروه حشره‌شناسی کشاورزی، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
چکیده
تاثیر یک مولفه جوی یا متغیر هواشناسی بر میزبان، پاتوژن یا تعامل آن‌ها در محیط‌های کنترل‌شده، بخش عمده‌ای از تاثیر تغییرات اقلیمی بر آفات و بیماری‌های گیاهان را به خود اختصاص داده است. تغییرات اقلیمی می‌تواند منجر به تغییراتی در مراحل و سرعت رشد آفات و بیماری‌ها، مقاومت میزبان و فیزیولوژی تعاملات میزبان-پاتوژن یا میزبان-آفت شود که می‌تواند باعث آسیب قابل‌توجه و کاهش عملکرد محصول گوجه‌فرنگی گردد. در سال‌های گذشته، رویکردهای مختلف در پیش‌بینی دقیق آفات و بیماری‌ها ناموفق بوده‌اند. ولی پیشرفت قابل‌توجه در شبکه‌های عصبی کانولوشن عمیق (DCNN)، باعث ایجاد انقلابی در تشخیص زودهنگام آفات و بیماری‌ها در محصولات کشاورزی شده است. این مدل‌های هوش مصنوعی با تجزیه و تحلیل مقادیر زیادی از داده‌های اقلیمی حال حاضر و گذشته، در کنار شناسایی اشیا و طبقه‌بندی تصاویر، می‌توانند شیوع بیماری‌ها را با دقت چشمگیری پیش‌بینی کنند. با این حال، درک ریزاقلیم خاص مناسب برای هر آفت و بیماری برای مداخله موثر بسیار مهم است. ترکیب دو عنصر داده‌های اقلیمی با پردازش تصویر، یک رویکرد قدرتمند و هدفمند برای حفاظت از محصولات ایجاد می‌کند. یک سیستم هشداردهنده می‌تواند به کاهش استفاده از آفت‌کش‌ها کمک کند و در نتیجه هزینه تولید محصول و آلودگی محیط‌زیست را کاهش دهد. برای پیش‌بینی بهتر آفات و بیماری‌ها در شرایط مختلف، باید از سرورهای ابری مناسب و شبکه‌های حسگر مبتنی بر اینترنت اشیا استفاده کرد.
کلیدواژه‌ها
CNN؛ پیش‌بینی آفات؛ خرد اقلیم؛ گوجه‌فرنگی؛ یادگیری ماشین
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