Analysis of Weed Growth in Rabi Crop Agriculture Using Deep Convolutional Neural Networks

Weed interference for the duration of crop establishment is a severe difficulty for wheat in North India [22.9734 ° N, 78.6569 ° E]. In situ far-flung detection for precision herbicide application minimizes the danger of both crop damage and herbicide input. This research paper focuses on the comparative study of crop growth and its effect at three different places in Madhya Pradesh [24.5840° N, 81.5020° E] India[20.5937° N, 78.9629° E]. These weed species included Pigweed (Amaranthaceae ), Goosefoot [Chenopodiaceae], Wild oat species (Poaceae), livid amaranth (Amaranthus blitum L.), Fathen[Chenopodiaceae (L.) Wild.], and Bermuda grass (Poaceae L.) a significant weed for rabi crop production in India with sensitivity to clopyralid, is the best available put up broadleaf herbicide. The intention of the Takes a look to assess the accuracy of four different CNNs architectures to locate the weed images of the Rabi crop of the family of various Rabi crops growing in competition with Rabi crops at 3 sites in Madhya Pradesh. Four CNNs have been compared, including object detection-primarily based ResNet-50, image classification-based VGGNet-16, Inception v4 and EfficientNet-B7 the EfficientNet-B7 networks have been trained to hit upon both leaves or canopies Everlasting of weeds. Image classification the use of ResNet-50 and VGGNet-16 was largely unsuccessful all through validation with whole pics (Fl-score < 0.04). CNN training elevated the usage of cropped photographs Eternal Broad Fall detection at some stage invalidation for VGGNet (F1-score = 0.77) and ResNet-50 (F1-Score = 0.62). The rabi crop weed leaf-trained inception V4 and EfficientNet-B7 achieved the highest F1-Score (0.94) and F1 Score (0.96) respectively, The aim of leaf-based EfficientNet-B7 extended false positives, even though such errors could be won over with extra training images for network desensitization training. Photograph-based faraway sensing rabi crop will become the most viable CNN test for weeds in competition with the EfficientNet-B7 crop.