A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants

With the advances in technology, computer vision applications using deep learning methods like Convolutional Neural Networks (CNNs) have been extensively applied in agriculture. Deploying these CNN models on mobile phones is beneficial in making them accessible to everyone, especially farmers and agricultural extension officers. This paper aims to automate the detection of damages caused by a devastating tomato pest known as Tuta Absoluta. To accomplish this objective, a CNN segmentation model trained on a tomato leaf image dataset is deployed on a smartphone application for early and real-time diagnosis of the pest and effective management at early tomato growth stages. The application can precisely detect and segment the shapes of Tuta Absoluta-infected areas on tomato leaves with a minimum confidence of 70% in 5 seconds only.

Analysis and Implementation of Fruit/Leave Disease Detection using Image Processing and Neural Approach

The latest generation of convolution neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of fruit/plant disease detection model, based on leaf image processing and classification, by the use of ANN. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

Accurate recognition of tomato diseases is of great significance for agricultural production. Sufficient and insufficient training data of supervised recognition neural network training are symmetry problems. A high precision neural network needs a large number of labeled data, and the difficulty of data sample acquisition is the main challenge to improving the performance of disease recognition. [l.]Moreover, the traditional data augmentation based on geometric transformation can obtain less information, and the generalization is not strong. In order to generate leaves with obvious disease feature and improve the performance of disease recognition, this paper analyzes and solves the problem of insufficient training samples in recognition network training, and proposes a new data augmentation method RAHC_GAN based on GAN, which is used to expand data and identify diseases. First, the proposed hidden variable is used to control the size of the disease area continuously, and the residual attention blocks are used to make the generated adversarial network pay more attention to the disease region in the leaf image, besides, a multi-scale discriminator is used to enrich the detailed texture of the generated image. Then, an expanded data set including original training set images and generated images by RAHC_GAN is established, which is used as the input of four kinds classification networks AlexNet, VGGNet, GoogLeNet and ResNet for performance evaluation. Experimental results show that RAHC_GAN can generate leaves with obvious disease feature, and the generated expanded data set can significantly improve the recognition performance of the classifier. After data augmentation, the recognition effect on the four classifiers is increased by 1.8%, 2.2%, 2.7%, and 0.4% respectively, which are higher than the comparison method. At the same time, the impact of expanded data with different ratio on the recognition performance was evaluated, and the method was extended to apple and grape diseased leaves. The proposed data augmentation method can simulate the distribution of tomato leaf diseases and improve the performance of disease recognition, and it may be extended to solve the problem of insufficient data in other plant research tasks.The tomato leaf data augmented by the traditional data augmentation methods based on geometric transformation usually contain less information, and the generalization is not strong. Therefore, a new data augmentation method, RAHC_GAN, based on generative adversarial networks is proposed in this paper, which is used to expand tomato leaf data and identify diseases. In this method, continuous hidden variables are added at the input of the generator, and the purpose is to continuously control the size of the generated disease area and to supplement the intra class information of the same disease. Additionally, the residual attention block is added to the generator to make it pay more attention to the disease region in the leaf image; a multi-scale discriminator is also used to enrich the detailed texture of the generated image and finally generate leaves with obvious disease features. Then, we use the images generated by RAHC_GAN and the original training images to build an expanded data set, which is used to train four kinds of recognition networks, AlexNet, VGGNet, GoogLeNet, and ResNet, and the performance is evaluated through the test set. Experimental results show that RAHC_GAN can generate leaves with obvious disease features, and the generated expanded data set can significantly improve the recognition performance of the classifier. Furthermore, the results of the apple, grape, and corn data set show that RAHC_GAN can also be used as a method to solve the problem of insufficient data in other plant research tasks.

Disease Detection and Remote Monitoring in Chilli Crop Using Image Processing

Observations today have verified that the average crop yield in India is declining due to illnesses that have affected fully grown plants. Chilli plant production is tough due to the plant's vulnerability to a variety of microorganisms, infectious illnesses, and pests. Infections in the chilli plant impact areas such as the leaves and stems. In the early stages of diagnosing chilli illnesses, leaf characteristics are examined. The leaf image is taken and analyzed to determine the health of the chilli plant. Pesticides are currently being tested on chilli plants on a regular basis without first determining the needs of each plant. This ensures that pesticides are only used when diseased plants are discovered. Keywords: Infections in the chilli plant, chilli illnesses, characteristics are examined, Pesticides are currently being tested on chilli plants.