Vision-Based Point Cloud Processing Framework for High Throughput Phenotyping

High throughput phenotyping is an emerging field that aims to bring rapid, non-invasive sensing technology in agriculture to accelerate the estimation of plant traits significantly. This paper presents a computer vision-based automated 3D point cloud processing framework for accurate estimation of essential phenotypic traits. The framework relies on three steps involving sub-plot detection, extraction of the crop from each sub-plot, and estimating the required trait. Four essential phenotypic traits are estimated as a use case of the proposed framework, namely, plant height, leaf area index (LAI), leaf inclination, and plant count. The crop of interest is mung bean. The obtained estimates for plant height, leaf area index (LAI), and leaf inclination are statistically validated by comparing the results with ground truth data in terms of coefficient of determination, root mean squared error (RMSE), and correlation coefficient. These metrics are found to be, on average, 0.87, 0.05, and 0.93 respectively. The regression analysis has also been performed to gain analytical insights into the data. For plant count, deep learning based segmentation method have been explored and the best accuracy achieved is 86%

Fully automated region of interest segmentation pipeline for UAV based RGB images

Unmanned Aerial Vehicles (UAVs) have exhibited its potential for efficient and non-invasive crop data acquisition in high throughput crop phenotyping. In general, for analysis of phenotypic traits, there is a need for extracting the region of interest (RoI) from images captured by UAVs. It involves the generation of orthomosaic, which is a complicated and time-intensive process. In this study, a fully automated AI-based pipeline has been proposed for the RoI segmentation from raw RGB images acquired via UAV. The proposed pipeline achieves a near real-time processing speed compared to the other baseline methods. The key feature of the pipeline is the introduction of Sub-Paths, in which the original UAV flight path is divided into several small paths which facilitates parallel processing. The image quality of the extracted RoI has been examined using blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE). The performance of the proposed pipeline is exemplified with the Leaf Area Index (LAI) estimation on five datasets containing three different crop types and growth stages. Regression analysis has also been performed on the estimated LAI values. Average R2, RMSE, and correlation scores of the estimates are observed to be 0.68, 0.033, and 0.83, respectively. © 2021 IAgr

Efficient Processing Methodology for UAV Flight Path Detection

Unmanned Areal Vehicle (UAV) based imagery is an emerging technology that has penetrated numerous verticals such as remote sensing, precision agriculture, land surveying. Various types of sensors are mounted onto UAV, and the images of the area of interest are captured. To get a complete distortionless areal view of the area, an orthomosaic is created using the captured images on which further analysis is done. But the traditional orthomosaic creation techniques are tedious, time-consuming, and also computationally complex. In this paper, a novel algorithm is proposed which speeds up the region of interest (ROI) detection significantly. In this method, the UAV flight path is divided into multiple Sub-Paths, and each path is processed parallelly. This method is universal and drastically improves the processing speed for any set of UAV images. It is observed that the algorithm reduces the computation time by around 75%

Cloud based Low-Power Long-Range IoT Network for Soil Moisture monitoring in Agriculture

The intervention of sensors and wireless networks has transformed cliched agricultural practices. Internet of Things (IoT) has penetrated various verticals, with agriculture being one of them. The application of IoT in agriculture is primarily focused on field parameter monitoring and automation, which aims to help farmers increase crop yield. Long-range and low-power devices, convenient installation, and cost-efficiency are the primary factors to be considered for deploying an IoT network in real-time. In this paper, we proposed a low-power long-range IoT network for monitoring of soil moisture. We have selected LoRa as the communication interface, which uses 868 MHz ISM band for signal transmission. The soil-moisture sensor and the LoRa nodes are designed in-house. Accuracy of the sensor nodes is tested by placing two nodes in the same sector. All the data collected are stored in the server and are available online

Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing

Agronomic inputs such as water , nutrients and fertilisers play a vital role in the health, growth and yield of crops. The lack of each of these inputs induces biotic and abiotic stress in the crop. Farmers are relying on groundwater because of decreased rainfall. The irrigation method can be improved by acquiring awareness of the health of crops and soils. In general, crop and soil quality is controlled by means of manual observation, which is time-consuming, labour-intensive and contributes to incorrect choices and substantial waste of resources. There is also an immediate need to automate the inspection process that will finally benefit farmers and agricultural scientists. In this paper, the identification of the water-stressed areas in the crop(maize) field has been studied, and an Unmanned Aerial Vehicle (UAV) based remote sensing is used to automate the crop health-monitoring process. We proposed a framework (model) based on Convolutional Neural Networks (CNN) to identify the stressed and normal/healthy areas in the maize crop field. The performance of the proposed framework has been compared with different models of CNN, such as ResNet50, VGG-19, and Inception-v3. The results show that the proposed model outperforms the baseline models and successfully classify stressed and normal areas with 95 % accuracy on train data and 93 % accuracy with 0.9370 precision and 0.9403 F1 score on test data