Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods

Diagnosis of Rice Diseases using Canny Edge K-means Clustering and Convolutional Neural Network based Transfer Learning

Recent breakthroughs in deep learning-based convolutional neural networks have significantly improved image categorization accuracy. Deep learning-based techniques for diagnosing illnesses from rice plant images have been created in this work, inspired by the realisation of CNNs in image classification. Smart monitoring technologies for the automatic identification of plant diseases are extremely beneficial to sustainable agriculture. Despite the fact that various mechanisms for plant disease categorization have been created in recent years, an inefficient technique based on evidence from picture samples is of concern for ground environments. In this study, an image processing technique for pre-processing and segmentation was used, as well as a multi-class convolutional neural network with transfer learning, to classify rice plant leaf diseases such as brown spot, hispa, leaf blast, and healthy class. The contaminated area was automatically separated from the healthy areas of the image using canny edge detection and k-means clustering, and the features were retrieved using the CNN model. In the experimental results, the CNN model without transfer learning is compared to the transfer learning model. VGGNet transfer learning is used to construct a multi-classification framework for each class of rice illness. The overall accuracy acquired by the CNN model without transfer learning is 92.14%, whereas the accuracy obtained by the transfer learning model is 94.80%.The current work demonstrates that the proposed technique is compelling and capable of recognizing rice plant illness for four classes

Customized CNN Model for Multiple Illness Identification in Rice and Maize

Crop diseases imperil global food security and economies, demanding early detection and effective management. Convolutional Neural Networks (CNNs), particularly in rice and maize leaf disease classification, have gained traction due to their automatic feature extraction capabilities. CNN models eliminate manual feature extraction, enabling precise disease diagnosis based on learned features. Researchers have rapidly advanced these models, achieving promising results. Leaf disease characteristics like color changes, texture variations, and lesion appearance have been identified as useful for automated diagnosis using machine learning. Developing CNN models involves crucial stages: dataset preparation, architecture selection, hyperparameter tuning, and model training and evaluation. Diverse and accurately annotated datasets are pivotal, and appropriate CNN architecture selection, such as ResNet101 and XceptionNet, ensures optimal performance. These architectures' pre-training on vast image datasets enhances feature extraction. Hyperparameter tuning fine-tunes the model, and training and evaluation gauge its precision. CNN models hold potential to enhance rice and maize productivity and global food security by effectively detecting and managing diseases

REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES

The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning

Autoencoders for semantic segmentation of rice fungal diseases

Received: January 4th, 2021 ; Accepted: March 22th, 2021 ; Published: March 31th, 2021 ; Correspondence: [email protected] the article, the authors examine the possibility of automatic localization of rice fungal infections using modern methods of computer vision. The authors consider a new approach based on the use of autoencoders - special neural network architectures. This approach makes it possible to detect areas on rice leaves affected by a particular disease. The authors demonstrate that the autoencoder can be trained to remove affected areas from the image. In some cases, this allows one to clearly highlight the affected area by comparing the resulting image with the original one. Therefore, modern architectures of convolutional autoencoders provide quite acceptable visual quality of detection

Generic Paddy Plant Disease Detector (GP2D2): An Application of the Deep-CNN Model

Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too

Review of the State of the Art of Transfer Learning for Plant Leaf Diseases Detection

Plant leaf diseases can have a significantly negative influence on the quantity and quality of agricultural cultivation, as well as the safety of food production. Plant leaf diseases could potentially entirely prevent the harvest of grains in some situations. Therefore, it is extremely important from a pragmatic standpoint to look for quick, automatic, cheap, and accurate ways to detect plant leaf diseases. One of the well-known plant leaf disease detection approaches is deep learning. Deep learning has several drawbacks as a result of the huge amount of data required to train the network. When a dataset has inadequate photographs, performance falls. An approach called "Transfer Learning" is an extensively used method for addressing the shortcomings of a small dataset, the length of the training process, and improving the performance of the model. In this study, we investigated transfer learning for deep CNNs to improve the learning capability to recognize leaf disease. This survey focuses on categorizing and analyzing the recent developments in transfer learning for Deep CNN situations to enhance learning performance by reducing the need for extensive training data collecting