Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93.18% and 92.87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively solved with dimensionality reduction, addition of height information as color, and consequent application of a 2D CNN-based approach. We found there to be a wide gap in the use of ML on 3D information. This gap will have to be addressed, especially for more complex plant feature extractions, which we intend to implement through further research. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

CNN Based Water Stress Detection in Chickpea Using UAV Based Hyperspectral Imaging

Water is an important agronomic input, which plays a vital role in the health and yield of the crop. Water deficiency results in abiotic stress, early detection of water stress help in recovering the health of the crop. Hyperspectral imaging (HSI) sensors acquire rich spectral information of the objects in hundreds of narrow bands, are capable of identifying the change in canopy water content, which is crucial in predicting irrigation requirements of the crop. Due to the wide field of coverages, short revisiting periods, and high spectral resolutions, Unmanned Aerial Vehicle (UAV) based HSI techniques are suitable in precision agriculture. In this paper, water stress detection in chickpea canopy is presented using hyperspectral (HS) images acquired from UAV. The drought classification was performed in two ways, i. by considering selected water-sensitive bands, and ii. by considering the whole spectral bands of the HS images. A 3D-2D convolutional neural network (CNN) model is used to classify well-watered canopy from water-stressed one, and its performance is compared with that of a Support Vector Machine (SVM) and a 2D+1D CNN model in identifying water stress. We obtained the best classification accuracy of 95.44%, which shows the potential of HSI in successfully detecting water stress in chickpea. © 2021 IEEE

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