Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach

This research article was published by Taylor & Francis Group, 2021Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature

Plant lesion boundary delineation using lightweight deep learning with tweaking mechanism

Ever since the dawn of agriculture, the devastating consequences of plant disease inevitably impacted the crop cultivation quantitatively and qualitatively. One of the plant disease incidents happened in 2007 in Georgia which lead to a $539.74 million loss in the total revenue. Intuitively, it is essential to tackle the disease outbreaks as early as possible to diagnose the underlying cause. The detection and classification of diseases carried out by the plant pathologists are subjected to cognitive error. To alleviate direct human intervention, machine learning is undoubtedly the key to avert this downfall. Over the years, numerous neural networks have been proposed to improve the existing state-of-art. Nevertheless, minimal works have been done on segmenting the region of the disease from the leaf. On the other hand, one of the inherent issues in machine learning is “What is the optimal configuration for the network to gain the highest performance?”. Many researchers are probing, but no single solution can cater to all the models built for different purposes. The concept of fine-tuning is a critical step which generally left out of discussion due to divergence in solution. Hence, the first objective is to build a semantic segmentation network that create a salient map image tracking the boundary of the disease. The second objective is to regularize and optimize the built network to identify the optimal configuration. SegNet’s fully convolutional architecture with transfer learning is chosen as the semantic segmentation network. A total of 1000 early and late blights of potato and tomato samples from PlantVillage are fed to the model. To capture the best network, optimizers such as SGD, RMSProp and Adam are benchmarked with regularization techniques such as adaptive learning rate, dropout layer and weight & bias rates re-initialization. Afterwards, hyperparameters such as mini-batch, initial learning rate, momentum, gradient, L2 regularization, number of samples and number of epochs are tuned progressively. Throughout the tweaking process, the global accuracy and mean IoU have increased from 86.96% and 50.72% to 93.86% and 60.24% respectively. In addition, the comparison between SegNet and FCN has proven that the former architecture is lightweight and powerful in delineating the boundary of plant lesion. With the delineated lesion’s boundary, the manifestation along the leaf surface can be traced and appraised for pathological anatomy