House Plant Leaf Disease Detection and Classification Using Machine Learning

Hibiscus is a fantastic herb, and in Ayurveda, it is one of the most renowned herbs that have extraordinary healing properties. Hibiscus is rich in vitamin C, flavonoids, amino acids, mucilage fiber, moisture content, and antioxidants. Hibiscus can help with weight loss, cancer treatment, bacterial infections, fever, high blood pressure, lower body temperature, treat heart and nerve diseases. Automatic leaf disease detection is an essential task. Image processing is one of the popular techniques for the plant leaf disease detection and categorization. In this chapter, the diseased leaf is identified by concurrent k-means clustering algorithm and then features are extracted. Finally, reweighted KNN linear classification algorithms have been used to detect the diseased leaves categories.

Classification of Plant Leaves Using New Compact Convolutional Neural Network Models

Precision crop safety relies on automated systems for detecting and classifying plants. This work proposes the detection and classification of nine species of plants of the PlantVillage dataset using the proposed developed compact convolutional neural networks and AlexNet with transfer learning. The models are trained using plant leaf data with different data augmentations. The data augmentation shows a significant improvement in classification accuracy. The proposed models are also used for the classification of 32 classes of the Flavia dataset. The proposed developed N1 model has a classification accuracy of 99.45%, N2 model has a classification accuracy of 99.65%, N3 model has a classification accuracy of 99.55%, and AlexNet has a classification accuracy of 99.73% for the PlantVillage dataset. In comparison to AlexNet, the proposed models are compact and need less training time. The proposed N1 model takes 34.58%, the proposed N2 model takes 18.25%, and the N3 model takes 20.23% less training time than AlexNet. The N1 model and N3 models are size 14.8 MB making it 92.67% compact, and the N2 model is 29.7 MB which makes it 85.29% compact as compared to AlexNet. The proposed models are giving good accuracy in classifying plant leaf, as well as diseases in tomato plant leaves.

Effect of Various Rates of P from Alternative and Traditional Sources on Butterhead Lettuce (Lactuca sativa L.) Grown on Peat Substrate

Previous research indicated the potential use of struvite (STR) as an alternative source of phosphorus (P) in crop production. A greenhouse experiment was conducted to evaluate the effect of STR and triple superphosphate (TSP) on the growth and chemical composition of butterhead lettuce grown on peat substrate over a three-month period (May–July). Both alternative (STR) and conventional (TSP) fertilizers were applied at three rates: (1) recommended rate based on the elemental content of substrate and crop nutritional need; (2) reduced rate (50% lower than recommended); and (3) increased rate (50% higher than recommended). Unfertilized (control) plants were also grown in the pot experiment. As expected, fertilizer application tended to increase the content of heavy metals in the substrate. Thus, an increase in Zn, Pb, and Cu content in peat substrate was found following STR amendments. However, compared with unfertilized plants, the applied rates of the STR and TSP fertilizers did not increase the content of Cd and Cu in the plant leaf, while Hg content was below the detection limit. In addition, Zn content in the plant leaf significantly decreased following STR and TSP applications. In comparison to unfertilized plants, both alternative and conventional fertilizers increased the content of P and nitrate nitrogen (N-NO3−) in the plant leaf while their effect on Mg content was negligible. The increased rate of STR was the best fertilizer treatment because it produced the largest number of leaves, which were also characterized by the highest P content. Our findings showed that STR was an effective source of P in butterhead lettuce cultivation without adverse effects on heavy metal accumulation.

A Comparative Study of Anti-inflammatory Activity of Tecoma stans, Acalypha indica and Abutilon indicum Plant Leaf Extract

Introduction: Inflammation is said to be the response of the body to an injury. It is a body defence reaction to reduce or eliminate the spread of injurious agents. It is essential that steps should be taken to introduce new medicinal plants and to develop cheaper, effective and safe analgesic and anti-inflammatory drugs. The main aim of this study is to assess the potential anti-inflammatory activity of Tecoma stans, Acalypha indica and Abutilon indicum plant is being studied. Materials and Methods: Protease inhibition assay was done by Bovine serum albumin was added to plant samples with increase in concentrations as per the standard methods. In this study, Aspirin was used as a standard anti-inflammatory drug.The data were analyzed statistically by a one-way analysis of variance (ANOVA) followed by Duncan’s multiple range test to see the statistical significance among the groups. The results with p<0.05 level were considered to be statistically significant. Results: In this study, it was observed that the plant leaf extract of Tecoma stans, Acalypha indica and Abutilon indicus contain anti-inflammatory activity. The protein denaturation inhibitory activity of leaf extract of Tecoma stans, Acalypha indica and Abutilon indicum, plant extract was represented graphically. Tecoma stans, was observed to contain the anti inflammatory activity. Conclusion: This study revealed that Tecoma stans, Acalypha indica and Abutilon indicum are important medicinal plants with diverse pharmacological spectrum and contain anti-inflammatory properties. Hence, this research has been taken to collect and compile the pharmacological uses of these plant extracts which will be useful to the society to venture into a field of alternative systems of medicine.

A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest

Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains.

PLRSNet: a semantic segmentation network for segmenting plant leaf region under complex background

PurposeExtraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.Design/methodology/approachExtracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.FindingsThe proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.Originality/valueIn this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.