AgriPredict: An Integrated Machine Learning Framework for Crop Price Forecasting and Leaf Disease Identification

Accurate identification of crop leaf diseases is essential for sustainable agriculture, yet variations in leaf morphology and disease symptoms across species remain challenging for traditional machine learning approaches. This study proposes a meta-learning–based stacking ensemble that integrates Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest Classifiers (RFC) for multi-species leaf pathology recognition, evaluated on apple and tomato datasets. Each base model independently learns discriminative visual features, and their class-probability outputs are fused through an RBF-SVM meta-learner. Experimental results show that individual models achieve accuracies of 89% (CNN), 88% (SVM), and 85% (RFC), while the proposed stacking ensemble significantly outperforms them with an accuracy of 98% and macro and weighted F1-scores of 0.975. These results demonstrate the effectiveness of heterogeneous meta-learning ensembles in enhancing robustness and generalization for multi-species crop disease classification.

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

Plants are considered an energy supply to humanity. Plant diseases can damage farming, reducing harvest yields. This immediately affects farmers’ income and human health. Plant disease identification is one of the world’s most extensive challenges for farmers. Thus, leaf disease identification is vital in agriculture. Traditional disease detection approaches are difficult to detect in large numbers of plant leaf infectious illnesses. The ability to identify plant leaf diseases using images is rapidly improving. However, processing plant leaf images is difficult due to their complicated structure and shape. While modern deep learning algorithms can categorize and diagnose plant sickness, preparing plant leaf images is widely acknowledged as the most important and hardest step. Preprocessing has a big impact on the final results of deep learning. However, the proposed vision-based approaches efficiently detect and observe illness’s external aspects. We have used the New Plant Diseases Dataset in our paperwork. We proposed deep learning with a specially trained convolutional neural network (CNN), which can aid in the classification of plant leaf images. Our approach makes use of a CNN architecture that was trained on a collection of these plant leaf images. This method accurately classifies plant leaf images into 38 types of plant leaf diseases with 99.29% test accuracy, outperforming approaches previously defined as state-of-the-art.

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

Identifying tomato leaf diseases under real field conditions using convolutional neural networks and a chatbot

To identify six types of beetroot leaf diseases across four clients, this research article thoroughly investigates the implementation of federated learning utilizing convolutional neural networks (CNNs). This work dramatically improves the preventative measures used to control these illnesses by utilizing the potential of decentralized learning to simplify the identification and categorization of leaf diseases from photos obtained locally at diverse customers' locations. With the data staying at the local customer locations, the offered technique addresses any possible privacy issues due to its one-of-a-kind, distinctive strength. All assessment measures for our CNN model—precision, recall, F1-score, and accuracy—showed consistently strong performance. The local data from each customer showed impressive outcomes in the client-specific result analysis. For instance, in Cl_q, the model's precision, recall, F1-score, and accuracy scores reached 95.59%, 96.03%, 95.61%, and 0.98, respectively, proving the model's dependability.Furthermore, the four customers' global averages performed very well across macro, weighted, and micro averages, with accuracy in the early to mid-nineties. For example, the micro standards varied between 92.56% (Cl_x) and 94.66% (Cl_q), whereas the macro averages among customers ranged from 92.59% (Cl_x) to 94.68% (Cl_q). The research's incremental performance gain from Cl_x to Cl_q also illustrates the influence of data variety in a federated learning environment. This paper substantially contributes to the field by outlining a practical, effective, and privacy-preserving approach for identifying beetroot leaf diseases. As a result, this study provides a revolutionary method for identifying plant diseases, significantly reducing the time required for diagnosis, reducing human error, and enabling prompt action.

In the field of agricultural information, the automatic identification and diagnosis of maize leaf diseases is highly desired. To improve the identification accuracy of maize leaf diseases and reduce the number of network parameters, the improved GoogLeNet and Cifar10 models based on deep learning are proposed for leaf disease recognition in this paper. Two improved models that are used to train and test nine kinds of maize leaf images are obtained by adjusting the parameters, changing the pooling combinations, adding dropout operations and rectified linear unit functions, and reducing the number of classifiers. In addition, the number of parameters of the improved models is significantly smaller than that of the VGG and AlexNet structures. During the recognition of eight kinds of maize leaf diseases, the GoogLeNet model achieves a top - 1 average identification accuracy of 98.9%, and the Cifar10 model achieves an average accuracy of 98.8%. The improved methods are possibly improved the accuracy of maize leaf disease, and reduced the convergence iterations, which can effectively improve the model training and recognition efficiency.

A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

The disease identification using machine learning algorithms is a promising approach for improving plant health and crop yield, and has the potential to revolutionize the agricultural industry. Leaf disease identification by machine learning algorithms is a promising field in plant pathology and precision agriculture leading to better crop yields and increased sustainability in agriculture. In Deep learning based EfficientnetB3 model for various plants leaves. Leaf disease identification by machine learning algorithms involves the use of advanced computational method to automatically identify and classify various types of plant diseases based on the symptoms observed on their leaves. The machine learning algorithms in leaf disease identification offers several advantages over traditional methods improved accuracy, speed, and scalability than the manual inspection to reduce the time-consuming and error-prone, especially in large agricultural fields.

Early diagnosis and identification of plant leaf disease are important for long-term agriculture and maximum yield production. Deep Learning has developed as a powerful computing paradigm in the field of artificial intelligence, with the ability to handle a wide range of computer vision challenges. One of the deep learning architectures that suggest implicit results for image identification and object detection applications is the deep convolutional neural network (CNN). In this work, deep CNN models are used to identify and classify plant leaf diseases. We have used VGG16, VGG19, Inception V3 and analyzed their performance. Finally, machine learning techniques were used to determine if the new plant image was infected or not. The result verifies that using VGG16 model and LR classifier, an accuracy of 98.5% is obtained.

The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it's similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.

Efficiently managing plant leaf conditions in large agricultural areas requires automated detection of leaf diseases. This research presents an integrated approach utilizing Content-Based Image Retrieval (CBIR) systems for the automatic identification of plant leaf diseases. The CBIR system employs preprocessing, segmentation using the graph cut superpixel method and feature extraction via 2D Convolutional Neural Network (CNN) models, which is focused primarily on detecting colorful plant flake complaints prevalent in blueberry, cherry and apple crops. Additionally, Gradient Boosting Trees (GBT) serve as classifiers to point to database stores of class predictions made by the classifiers for plant leaf images. Comparable deep learning models such as InceptionV3 and ResNet50 in the proposed CBIR system extract high-level features from images, predict leaf classes and store them in the database. Euclidean similarity measures between feature representations further assess the CBIR system's overall performance, with deep learning models successfully supporting retrieval and image classification tasks when compared with 2D CNN models.

The disease and pest recognition algorithms based on computer vision can automatically process and analyze a large amount of disease and pest images, thereby achieving rapid and accurate identification of disease and pest categories on crop leaves. Currently, most studies use deep learning models for feature extraction and identification of crop leaf disease and pest images. However, these methods are often seen as “black box” model, making it difficult to interpret the basis for their specific decisions. To address this issue, we propose an intrinsically interpretable crop leaf disease and pest identification model named Contrastive Prototypical Part Network (CPNet). The idea of CPNet is to find the key regions that influence the model’s decision by calculating the similarity values between the convolutional feature maps and the learnable latent prototype feature representations. Moreover, because the limited availability of data resources for crop leaf disease and pest images, we employ a supervised contrastive learning strategy to capture the similar information between examples in one class and contrast them with examples in other classes. Finally, we evaluate our approach on four publicly available datasets, and the experimental results demonstrate that our proposed CPNet not only achieves improvements in performance over baseline methods across multiple datasets, but also provides interpretable evidence for crop leaf disease and pest identification.

Using a multi-convolutional neural network to automatically identify small-sample tea leaf diseases

This paper focuses on review of deep learning techniques on plant leaf disease detection and its diagnosis. As we know agricultural farming plays a crucial role in world economy but the sad part is, it is adversely affected by damage due to diseases to plants all around the world. It is observed, plants are one of important source of energy for human being as well as other animals, and hence it is prime to save not only plant but their leaves too. It is then important to identify infected leaves which are affected by disease; this will be great help for farmers to protect it from sowing till process of harvesting which can reflect in reduction in economic loss. But manual work in this regard will be absolute burden on labour hence Automatic identification of diseases and it’s diagnose will be add-on tool for agricultural yield and it will also help to maximize the production of crop. In this paper, comparative analysis between the performances of distinguish deep learning approaches for identification and diagnosis of various diseases on plant leaves with the help of different patterns of plant leaf images is discussed. In many experiments and evaluations, process of segmentation, feature extractions and classification methods are being done for quick diagnosis on selected plant leaf diseases. Here, we are trying to help farmers to identify and diagnose the disease on banana leaves by using deep convolutional neural network which can be treated at early stage.