Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the yield prediction problem there exist the following inadequacies: Unable to create a direct non-linear or linear mapping between the raw data and crop yield values; and the performance of those models highly relies on the quality of the extracted features. Deep reinforcement learning provides direction and motivation for the aforementioned shortcomings. Combining the intelligence of reinforcement learning and deep learning, deep reinforcement learning builds a complete crop yield prediction framework that can map the raw data to the crop prediction values. The proposed work constructs a Deep Recurrent Q-Network model which is a Recurrent Neural Network deep learning algorithm over the Q-Learning reinforcement learning algorithm to forecast the crop yield. The sequentially stacked layers of Recurrent Neural network is fed by the data parameters. The Q- learning network constructs a crop yield prediction environment based on the input parameters. A linear layer maps the Recurrent Neural Network output values to the Q-values. The reinforcement learning agent incorporates a combination of parametric features with the threshold that assist in predicting crop yield. Finally, the agent receives an aggregate score for the actions performed by minimizing the error and maximizing the forecast accuracy. The proposed model efficiently predicts the crop yield outperforming existing models by preserving the original data distribution with an accuracy of 93.7%.

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Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition.

Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization. For classification, we assessed and validated the accuracy of our methods on a dataset of 54 3D shoot architectures, representing multiple growth conditions and developmental time points for two Solanaceous species, tomato (Solanum lycopersicum cv 75 m82D) and Nicotiana benthamiana. Using deep learning, we classified lamina versus stems with 97.8% accuracy. Critically, we also demonstrated the robustness of our method to growth conditions and species that have not been trained on, which is important in practical applications but is often untested. For lamina counting, we developed an enhanced region-growing algorithm to reduce oversegmentation; this method achieved 86.6% accuracy, outperforming prior methods developed for this problem. Finally, for stem skeletonization, we developed an enhanced tip detection technique, which ran an order of magnitude faster and generated more precise skeleton architectures than prior methods. Overall, our improvements enable higher throughput and accurate extraction of phenotypic properties from 3D point cloud data.

/pdf/machine-learning-approaches-to-improve-three-basic-plant-1knfgl43l4.pdf

Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds

Plant species recognition methods using leaf image: Overview

The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches

Method of plant leaf recognition based on improved deep convolutional neural network

Plant species can be distinguished mainly on the basis of the identification of plant leaf characteristics Nevertheless, most identification systems show poor performance when small targets, including plant leaves and the like, are detected under a complex background In order to improve the identification ability of plant leaves in a complex environment The invention puts forward a plant leaf identification method based on deep learning Inception V2 with BN (Batch Normalization) is used for replacing a convolutional neural layer in a Faster RCNN (Region Convolutional Neural Network) to provide multi-scale image features for an RPN (Region Proposal Network) In addition, an original image is firstly segmented into an appointed size according to a grid, and the segmented images are loaded to a network which is put forward in sequence Through the accurate classification of Softmax and bounding box regressor, and the segmented imaged with identification tags are spliced to obtain a final image An experiment result identifies leaves under the complex background The method has higher identification accuracy than the Faster RCNN

Plant leaf identification method based on deep learning

An improved Canny algorithm-based automatic obtaining threshold edge detection method comprises the following steps of utilizing an improved adaptive median filter to substitute for a Gauss filter ina conventional Canny algorithm to denoise; when an amplitude is solved, using eight directional gradient templates to calculate the amplitude of an image gradient; generating the high and low thresholds adaptively via an Otsu algorithm, utilizing the double thresholds to detect and connect the edges, and refining the edges via a morphological method, thereby obtaining a final edge image. The method of the present invention can effectively solve the problems that a conventional first-order derivative edge detection method is short in edge, loses the edges, and is sensitive to the noise, etc., enables the detected edges to be positioned accurately, is good in continuity, can effectively inhibit the pseudo edges while detecting more edge details, and avoids a problem of inputting the thresholds artificially and manually. The method has a better real-time performance in the log image detection, and can be used in a real-time image analysis system.

Zhu Xiaolong

Papers

Method of plant leaf recognition based on improved deep convolutional neural network

Plant leaf identification method based on deep learning

Improved Canny-based automatic threshold edge detection method