Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

Plant diseases are a crucial issue in agriculture. An accurate and automatic identification of leaf diseases could help to develop an early response to reduce economic losses. Recent research in plant diseases has adopted deep neural networks. However, such research has used the models as a black-box passing the labeled images through the networks. This letter presents an analysis of the network weights for the automatic recognition of soybean leaf diseases applied to images taken straight from a small and cheap unmanned aerial vehicle (UAV). To achieve high accuracy, we evaluated four deep neural network models trained with different parameters for fine-tuning (FT) and transfer learning. Data augmentation and dropout were used during the network training to avoid overfitting. Our methodology consists of using the SLIC method to segment the plant leaves in the top-view images obtained during the flight. We tested our data set created from real flight inspections in an end-to-end computer vision approach. Results strongly suggest that the FT of parameters substantially improves the identification accuracy.

Soybean has been the main Brazilian agricultural commodity, contributing substantially to the country’s trade balance. However, foliar diseases are the key factor that can undermine the soy production, usually caused by fungi, bacteria, viruses, and nematodes. This letter proposes a computer vision system to track soybean foliar diseases in the field using images captured by the low-cost unmanned aerial vehicle model DJI Phantom 3. The proposed system is based on the segmentation method Simple Linear Iterative Clustering to detect plant leaves in the images and on visual attributes to describe the features of foliar physical properties, such as color, gradient, texture, and shape. Our methodology evaluated the performance of six classifiers for different heights, including 1, 2, 4, 8, and 16 m. Experimental results showed that color and texture attributes lead to higher classification rates, achieving the precision of 98.34% for heights between 1 and 2 m, with a decay of 2% at each meter. Results indicate that our approach can support experts and farmers to monitor diseases in soybean fields.

The occurrence of insect pest attacks in soybean fields has worried farmers around the world. Early and automatic diagnosis of insect pests number could assess the infestation level of each plantation area to optimize the applications of pesticides in the crop and, consequently, reduce production costs and environmental impact. Recent research on insect count has adopted deep neural networks. However, researches have employed models trained to count only one species of insect, using images captured in a controlled environment, quite different from a real scenario. In order to obtain high accuracy, we evaluated three models of convolutional neural networks (CNNs) with three different training strategies: 100% fine-tuning with the weights obtained from ImageNet, a complete network with the weights initialized randomly and transfer learning with the weights obtained from ImageNet. Data augmentation and dropout were used during network training to reduce overfitting and increase generalization of the model. Our approach consists in segmenting an image from the plantation with the simple linear iterative clustering (SLIC) method and classifying each superpixel segment into a pest insect class using the CNN-trained classification model. The pest insect count is obtained by adding the insects of each superpixel class identified by our computer vision system. The results indicate that the deep-learning models can be used successfully to support specialists and farmers in the insect pest management in soybean fields.