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Fruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified YOLOv3 model called YOLO-Tomato models were adopted to detect tomatoes in complex environmental conditions. With the application of label what you see approach, densely architecture incorporation, spatial pyramid pooling and Mish function activation to the modified YOLOv3 model, the YOLO-Tomato models: YOLO-Tomato-A at AP 98.3% with detection time 48 ms, YOLO-Tomato-B at AP 99.3% with detection time 44 ms, and YOLO-Tomato-C at AP 99.5% with detection time 52 ms, performed better than other state-of-the-art methods.

Modern commercial tomato varieties are substantially less flavorful than heirloom varieties. To understand and ultimately correct this deficiency, we quantified flavor-associated chemicals in 398 modern, heirloom, and wild accessions. A subset of these accessions was evaluated in consumer panels, identifying the chemicals that made the most important contributions to flavor and consumer liking. We found that modern commercial varieties contain significantly lower amounts of many of these important flavor chemicals than older varieties. Whole-genome sequencing and a genome-wide association study permitted identification of genetic loci that affect most of the target flavor chemicals, including sugars, acids, and volatiles. Together, these results provide an understanding of the flavor deficiencies in modern commercial varieties and the information necessary for the recovery of good flavor through molecular breeding.

The extensive use of chemical fertilizers poses serious collateral problems such as environmental pollution, pest resistance development and food safety decline. Researches focused on applying plant-beneficial microorganisms to partially replace chemical fertilizer use is increasing due to the requirement of sustainable agriculture development. Thus to investigate the possibility of a plant-beneficial Trichoderma strain and its bio-organic fertilizer product in saving chemical fertilizer application and in improving crop quality, a field trial and continuous pot experiments were carried out with tomato. Four treatments were set up: a reduced application of chemical fertilizer (75% of the conventional application) plus Trichoderma -enriched bio-organic fertilizer (BF), organic fertilizer (OF) or Trichoderma spore suspension (SS), with using the 100% rate of the conventional chemical fertilizer as the control (CF). The results showed that the total soluble sugar, Vitamin C and nitrate accumulations were, respectively, +up to 24%, +up to 57% and –up to 62% in the tomatoes of the BF treatment compared to those of the control (CF). And both of the pot and field trials revealed that reduced rates of chemical fertilizer plus bio-organic fertilizer produced tomato yields equivalent to those obtained using the 100% of the chemical fertilizer. However, application with the inoculant alone (SS) or combined with the organic fertilizer alone (OF) would lead to a yield decreases of 6–38% and 9–35% over the control. Since the increased abundance of soil microflora and the enhanced soil fertility frequently showed positive linear correlations especially in the BF-treated soils, we conclude that the efficacy of this bio-organic fertilizer for maintaining a stable tomato yield and improving tomato quality may be due to the improved soil microbial activity. Thus, the results suggest that the Trichoderma bio-organic fertilizer could be employed in combination with the appropriate rates of chemical fertilizers to get maximum benefits regarding yield, quality and fertilizer savings.

The individual role of biochar, compost and PGPR has been widely studied in increasing the productivity of plants by inducing resistance against phyto-pathogens. However, the knowledge on combined effect of biochar and PGPR on plant health and management of foliar pathogens is still at juvenile stage. The effect of green waste biochar (GWB) and wood biochar (WB), together with compost (Comp) and plant growth promoting rhizobacteria (PGPR; Bacillus subtilis ) was examined on tomato ( Solanum lycopersicum L.) physiology and Alternaria solani development both in vivo and in vitro. Tomato plants were raised in potting mixture modified with only compost (Comp) at application rate of 20% (v/v), and along with WB and GWB at application rate of 3 and 6% (v/v), each separately, in combination with or without B. subtilis . In comparison with WB amended soil substrate, percentage disease index was significantly reduced in GWB amended treatments (Comp + 6%GWB and Comp + 3%GWB; 48.21 and 35.6%, respectively). Whereas, in the presence of B. subtilis disease suppression was also maximum (up to 80%) in the substrate containing GWB. Tomato plant growth and physiological parameters were significantly higher in treatment containing GWB (6%) alone as well as in combination with PGPR. Alternaria solani mycelial growth inhibition was less than 50% in comp, WB and GWB amended growth media, whereas B. subtilis induced maximum inhibition (55.75%). Conclusively, the variable impact of WB, GWB and subsequently their concentrations in the soil substrate was evident on early blight development and plant physiology. To our knowledge, this is the first report implying biochar in synergism with PGPR to hinder the early blight development in tomatoes.

Deep learning establishes an ongoing, modern technique for image processing with large potential and promising results. After proving its efficiency in various applications DL has also entered into the domain of agriculture. Here, we surveyed 38 research works that applied deep learning techniques to various research problems in tomato plant. We examine the areas of tomato plant research where deep learning is applied, data preprocessing techniques applied, transfer learning and augmentation techniques used. Studied dataset information like data sources used, number of images, classes and train test validation ratio applied. In addition, we study comparisons done on various deep learning architectures and discussed the outcome. The finding showed that DL techniques outperformed all other image processing techniques but DL performs mainly depends on the dataset used.

The use of pathogen-resistant cultivars is expected to increase yield and decrease fungicide use in agriculture. However, in potato breeding, increased resistance obtained via resistance genes (R-genes) is hampered because R-gene(s) are often specific for a pathogen race and can be quickly overcome by the evolution of the pathogen. In parallel, susceptibility genes (S-genes) are important for pathogenesis, and loss of S-gene function confers increased resistance in several plants, such as rice, wheat, citrus and tomatoes. In this article, we present the mutation and screening of seven putative S-genes in potatoes, including two DMR6 potato homologues. Using a CRISPR/Cas9 system, which conferred co-expression of two guide RNAs, tetra-allelic deletion mutants were generated and resistance against late blight was assayed in the plants. Functional knockouts of StDND1 , StCHL1, and DMG400000582 ( StDMR6-1 ) generated potatoes with increased resistance against late blight. Plants mutated in StDND1 showed pleiotropic effects, whereas StDMR6-1 and StCHL1 mutated plants did not exhibit any growth phenotype, making them good candidates for further agricultural studies. Additionally, we showed that DMG401026923 (here denoted StDMR6-2 ) knockout mutants did not demonstrate any increased late blight resistance, but exhibited a growth phenotype, indicating that StDMR6-1 and StDMR6-2 have different functions. To the best of our knowledge, this is the first report on the mutation and screening of putative S-genes in potatoes, including two DMR6 potato homologues.

Movement of RNAs between cells of a single plant is well documented, but cross-species RNA transfer is largely unexplored. Cuscuta pentagona (dodder) is a parasitic plant that forms symplastic connections with its hosts and takes up host messenger RNAs (mRNAs). We sequenced transcriptomes of Cuscuta growing on Arabidopsis and tomato hosts to characterize mRNA transfer between species and found that mRNAs move in high numbers and in a bidirectional manner. The mobile transcripts represented thousands of different genes, and nearly half the expressed transcriptome of Arabidopsis was identified in Cuscuta. These findings demonstrate that parasitic plants can exchange large proportions of their transcriptomes with hosts, providing potential mechanisms for RNA-based interactions between species and horizontal gene transfer.

Coffee is a valuable beverage crop due to its characteristic flavor, aroma, and the stimulating effects of caffeine. We generated a high-quality draft genome of the species Coffea canephora, which displays a conserved chromosomal gene order among asterid angiosperms. Although it shows no sign of the whole-genome triplication identified in Solanaceae species such as tomato, the genome includes several species-specific gene family expansions, among them N-methyltransferases (NMTs) involved in caffeine production, defense-related genes, and alkaloid and flavonoid enzymes involved in secondary compound synthesis. Comparative analyses of caffeine NMTs demonstrate that these genes expanded through sequential tandem duplications independently of genes from cacao and tea, suggesting that caffeine in eudicots is of polyphyletic origin.

With approximately 450 species, spiny Solanum species constitute the largest monophyletic group in the Solanaceae family, but a high-quality genome assembly from this group is presently missing. We obtained a chromosome-anchored genome assembly of eggplant ( Solanum melongena ), containing 34,916 genes, confirming that the diploid gene number in the Solanaceae is around 35,000. Comparative genomic studies with tomato ( S . lycopersicum ), potato ( S . tuberosum ) and pepper ( Capsicum annuum ) highlighted the rapid evolution of miRNA:mRNA regulatory pairs and R-type defense genes in the Solanaceae, and provided a genomic basis for the lack of steroidal glycoalkaloid compounds in the Capsicum genus. Using parsimony methods, we reconstructed the putative chromosomal complements of the key founders of the main Solanaceae clades and the rearrangements that led to the karyotypes of extant species and their ancestors. From 10% to 15% of the genes present in the four genomes were syntenic paralogs (ohnologs) generated by the pre-γ, γ and T paleopolyploidy events, and were enriched in transcription factors. Our data suggest that the basic gene network controlling fruit ripening is conserved in different Solanaceae clades, and that climacteric fruit ripening involves a differential regulation of relatively few components of this network, including CNR and ethylene biosynthetic genes.

Early detection of plant diseases is one of the main keys to handling diseases quickly and successfully. The purpose of this study is to find out a simpler CNN architecture and meet an acceptable compromise between accuracy and simplification to detect diseases in tomato plants based on leaf images. This simpler architecture will allow the development of standalone and independent system model in the field to classify and identify the tomato plants diseases in low price and limited resources. This proposed architecture was developed from the CNN architecture baseline and is intended to classify 10 classes of tomato leaves consist of one healthy class and 9 classes of leaves diseases taken from the Plant Village dataset. In this study, the performance of the proposed architecture and comparative architectures are examined in the same dataset. Comparative architectures used are some existing CNN architectures that are commonly used namely VGG Net, Shuffle Net and Squeeze Net. The results indicated that the proposed architecture can achieve competitive accuracy compared with the existing architecture while the proposed architecture is much shorter than the existing architecture and better in terms of performance time.