Weed detection in soybean crops using ConvNets

The use of Deep Learning for the detection of weeds in soybean crops is proposed.The approach uses ConvNets in images segmented by the SLIC Superpixels algorithm.An image database was created using photographs captured by UAVs.The performance of ConvNets were compared with others classifiers as SVM.Using ConvNets, this work achieved above 97% accuracy in weed detection. Weeds are undesirable plants that grow in agricultural crops, such as soybean crops, competing for elements such as sunlight and water, causing losses to crop yields. The objective of this work was to use Convolutional Neural Networks (ConvNets or CNNs) to perform weed detection in soybean crop images and classify these weeds among grass and broadleaf, aiming to apply the specific herbicide to weed detected. For this purpose, a soybean plantation was carried out in Campo Grande, Mato Grosso do Sul, Brazil, and the Phantom DJI 3 Professional drone was used to capture a large number of crop images. With these photographs, an image database was created containing over fifteen thousand images of the soil, soybean, broadleaf and grass weeds. The Convolutional Neural Networks used in this work represent a Deep Learning architecture that has achieved remarkable success in image recognition. For the training of Neural Network the CaffeNet architecture was used. Available in Caffe software, it consists of a replication of the well known AlexNet, network which won the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012). A software was also developed, Pynoviso, which through the use of the superpixel segmentation algorithm SLIC, was used to build a robust image dataset and classify images using the model trained by Caffe software. In order to compare the results of ConvNets, Support Vector Machines, AdaBoost and Random Forests were used in conjunction with a collection of shape, color and texture feature extraction techniques. As a result, this work achieved above 98% accuracy using ConvNets in the detection of broadleaf and grass weeds in relation to soil and soybean, with an accuracy average between all images above 99%.