Formula padat Bacillus cereus STRAIN TLE1.1 untuk pengendalian penyakit busuk pangkal batang (Sclerotium rolfsii) pada tanaman tomat

Penyakit utama tanaman tomat yaitu busuk pangkal batang yang disebabkan oleh Sclerotium rolfsii dapat menimbulkan kerugian mencapai 80-100%. Tujuan penelitian yaitu untuk mendapatkan formula padat Bacillus cereus strain TLE1.1 yang efektif untuk pengendalian penyakit busuk pangkal batang pada tanaman tomat. Penelitian bersifat eksperimen dengan mengamati kemampuan formula padat B. cereus strain TLE1.1 dalam pengendalian penyakit busuk pangkal batang dengan Rancangan Acak Lengkap yang terdiri atas 9 perlakuan dan 3 ulangan. Perlakuan terdiri atas kombinasi bahan pembawa formula padat yang terdiri atas limbah padat ampas tebu, ampas tahu dan tongkol jagung, fungisida serta kontrol. Masing-masing formula padat B. cereus strain TLE1.1 diintroduksi pada benih dan bibit tomat. Hasil penelitian menunjukkan bahwa hampir semua formula mampu menekan penyakit busuk pangkal batang tanaman tomat. Formula terbaik dalam menurunkan penyakit busuk pangkal batang pada tanaman yaitu formula ampas tahu dan ampas tahu + tongkol jagung.Main disease of tomato plant, namely stem rot caused by Sclerotium rolfsii which can cause losses up to 80-100%. The aim of the study was to obtain a solid formula of Bacillus cereus strain TLE1.1 which was effective for controlling stem rot disease in tomato plant. This research was an experimental study to know the ability of the solid formula of B. cereus strain TLE1.1 in controlling stem rot disease which was carried out in a completely randomizeddesign consisting of 9 treatments and 3 replications. The treatment consistedof a combination of solid formula carriers consisting of sugarcane solid waste,tofu dreg and corncob, fungicides and controls. Each solid formula of B. cereus strain TLE1.1 was introduced into tomato seeds and seedlings. The results showed that almost all of the formulas were able to suppress stem base disease of tomato plants. The best formula that reduced stem rot in plants were the tofu dreg and tofu dreg + corncob formula.

Morphological and molecular identification of Cladosporium sphaerospermum isolates collected from tomato plant residues

This study was conducted at the Agriculture College University of Karbala, Iraq to isolate and morphologically and molecularly diagnose thirteen Cladosporium isolates collected from tomato plant residues present in desert regions of Najaf and Karbala provinces, Iraq. We diagnosed the obtained isolates by PCR amplification using the ITS1 and ITS4 universal primer pair followed by sequencing. PCR amplification and analysis of nucleotide sequences using the BLAST program showed that all isolated fungi belong to Cladosporium sphaerospermum. Analysis of the nucleotide sequences of the identified C. sphaerospermum isolates 2, 6, 9, and 10 showed a genetic similarity reached 99%, 98%, 99%, and 99%, respectively, with those previously registered at the National Center for Biotechnology Information (NCBl). By comparing the nucleotide sequences of the identified C. sphaerospermum isolates with the sequences belong to the same fungi and available at NCBI, it was revealed that the identified C. sphaerospermum isolates 2, 6, 9, and 10 have a genetic variation with those previously recorded at the National Center for Biotechnology Information (NCBl); therefore, the identified sequences of C. sphaerospermum isolates have been registered in GenBank database (NCBI) under the accession numbers MN896004, MN896107, MN896963, and MN896971, respectively.

Morphological, Physiological and Biochemical Response to Low Temperature Stress in Tomato (Solanum lycopersicum L.): A Review

Growth and productivity are traumatized by the low temperature that triggers a series of physiological, morphological, molecular and biochemical changes in plants that eventually disturb plant life. Most of the cultivable lands of the world are adversely affected by temperature stress conditions which have an adverse impact on global tomato productivity. Plants undergo several water related metabolic activities for their survival during cold stress conditions. Understanding the morphological, physiological and biochemical reactions to low temperature is essential for a comprehensive view of the perception of tomato plant tolerant mechanism. This review reports some aspects of low temperature inflated changes in physiological and biochemical in the tomato plant. Low temperature stress influences the reproductive phases of plants with delayed flowering which enhance pollen sterility resultant drastically affects the harvest yield. It also decreases the capacity and efficiency of photosynthesis through changes in gas exchange, pigment content, chloroplast development and decline in chlorophyll fluorescence photosynthetic attributes. Amassing of osmoprotectant is another adaptive mechanism in plants exposed to low temperatures stress, as essential metabolites directly participate in the osmotic adjustment. Furthermore, low temperature stress enhanced the production of reactive oxygen species (ROS) which may oxidize lipids, proteins and nucleic acids which bring in distortion at the level of the cell. At the point when extreme reactive oxygen species produced, plants synthesize antioxidant enzymes and osmoprotectants that quench the abundance of reactive oxygen species. These reviews focus on the capacity and techniques of the tomato plant to react low temperature stress.

Effect of Data Augmentation in the Classification and Validation of Tomato Plant Disease with Deep Learning Methods

The paper discusses disease identification and classification in tomato plants, as well as the effect of data augmentation in deep learning models. The database used here is Tomato plant leaves (TPL) images from the PlantVillage Database in the healthy and disease classes. The disease categories have been chosen depending on their occurrence in the Indian States. The proposed ResNet50, ResNet18, and ResNet101 deep-learning model with transfer learning combined with the softmax classification are used to identify and categorize the tomato leaf images into the healthy or diseases classes in the dataset. The unique combination of including the noise and blur in the images and position and color data augmentation makes the dataset robust. Two different data augmentation methods are used for the classification problem, and significant improvement is seen in the classification accuracy with the proposed augmented dataset. The model’s success rate makes the model helpful in extending support in validating a model for identifying plant disease. The validation of models is done on PlantVillage and images taken at Krishi Vigyan Kendra Narayangaon, Pune, India. ResNet101 model trained with augmented dataset outperforms the testing accuracy of 99.99% and validation accuracy of 95.83%.

Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes

Recognizing plant diseases is a major challenge in agriculture, and recent works based on deep learning have shown high efficiency in addressing problems directly related to this area. Nonetheless, weak performance has been observed when a model trained on a particular dataset is evaluated in new greenhouse environments. Therefore, in this work, we take a step towards these issues and present a strategy to improve model accuracy by applying techniques that can help refine the model’s generalization capability to deal with complex changes in new greenhouse environments. We propose a paradigm called “control to target classes.” The core of our approach is to train and validate a deep learning-based detector using target and control classes on images collected in various greenhouses. Then, we apply the generated features for testing the inference of the system on data from new greenhouse conditions where the goal is to detect target classes exclusively. Therefore, by having explicit control over inter- and intra-class variations, our model can distinguish data variations that make the system more robust when applied to new scenarios. Experiments demonstrate the effectiveness and efficiency of the proposed approach on our extended tomato plant diseases dataset with 14 classes, from which 5 are target classes and the rest are control classes. Our detector achieves a recognition rate of target classes of 93.37% mean average precision on the inference dataset. Finally, we believe that our study offers valuable guidelines for researchers working in plant disease recognition with complex input data.