Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left"> </td><td class="align_center">Actual positive</td><td class="align_center">Actual negative</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">Predicted positive</td><td class="align_center">True positives (TP)</td><td class="align_center">False positives (FP)</td></tr><tr><td class="align_left">Predicted negative</td><td class="align_center">False negatives (FN)</td><td class="align_center">True negatives (TN)</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr><tr class="table-fn"><td><div>TP: examples correctly labeled as positives. FP: negative examples incorrectly labeled as positive. FN: positive examples incorrectly labeled as negative. TN: correspond to negatives correctly labeled as negative.<br/></div></td></tr></table>

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Table 2: Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests 