Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved Faster R-CNN

Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(Faster R-CNN).The model incorporates the following optimization strategies: first, Inception_Res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(RPN);third, region of interest(Ro I) alignment replaces Ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original Faster R-CNN and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original Faster R-CNN model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.

Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images

Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods.Nevertheless,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex backgrounds.Object detection algorithms are directly affected by these factors.This work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature distillation.Our feature-based knowledge distillation method allows us to compress the model without sacrificing its effectiveness.An imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s features.On the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.

Designing and Evaluating a Collaborative Knowledge Management Framework for Leaf Disease Detection

Knowledge Management(KM)has become a dynamic concept for inquiry in research.The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline,several KM frameworks have been designed to serve this purpose.This research aims to propose a Collaborative Knowledge Management(CKM)Framework that bridges gaps and overcomes weaknesses in existing frameworks.The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan.A software LCWU aKMS was developed which serves as a practical implementation of the concepts behind the proposed CKMF framework.LCWU aKMS served as an effective system for rice leaf disease detection and identification.It aimed to enhance CKM through knowledge sharing,lessons learned,feedback on problem resolutions,help from co-workers,collaboration,and helping communities.Data were collected from 300 rice crop farmers by questionnaires based on hypotheses.Jennex Olfman model was used to estimate the effectiveness of CKMF.Various tests were performed including frequency measures of variables,Cronbach’s alpha reliability,and Pearson’s correlation.The research provided a KMS depicting KM and collaborative features.The disease detection module was evaluated using the precision and recall method and found to be 94.16%accurate.The system could replace the work of extension agents,making it a cost and time-effective initiative for farmer betterment.

Visual detection of rice rows based on Bayesian decision theory and robust regression least squares method

Paddy field management is complicated and labor intensive.Correct row detection is important to automatically track rice rows.In this study,a novel method was proposed for accurate rice row recognition in paddy field transplanted by machine before the disappearance of row information.Firstly,Bayesian decision theory based on the minimum error was used to classify the period of collected images into three periods(T1:0-7 d;T2:7-28 d;T3:28-45 d),and resulting in the correct recognition rate was 97.03%.Moreover,secondary clustering of feature points was proposed,which can solve some problems such as row breaking and tilting.Then,the robust regression least squares method(RRLSM)for linear fitting was proposed to fit rice rows to effectively eliminate interference by outliers.Finally,a credibility analysis of connected region markers was proposed to evaluate the accuracy of fitting lines.When the threshold of credibility was set at 40%,the correct recognition rate of fitting lines was 96.32%.The result showed that the method can effectively solve the problems caused by the presence of duckweed,high-density inter-row weeds,broken rows,tilting(±60°),wind and overlap.